Trucking Hub

In my role as a startup CTO, I'm focused on the following:

- Built a company development department from the ground up.
- Setting up a local office in Belgrade.
- Growing engineering team from 0 to 10+ exceptional talents.
- Owner of the technology vision and roadmap.
- Driving collaboration with external partners.
- Laying the foundations of scaling the technical platform and scaling the teams.
- Establishing and executing technology strategy for delivering the most possible outcome for our customers.
- Growing a healthy, collaborative engineering culture aligned with the company values.
- Driving engineering excellence and ensuring teams are delivering high-quality results.
- Delivering the flagship product according to expectations.
- Ensuring the product roadmap is well thought out and feasible for each team.
- Understanding the teams’ impediments and removing them.
- Defining clear career (progression) paths.
- Regular 1:1 meetings.
- Helping people grow and progress in their careers.
- Regular, constructive performance feedback.
- Conducting performance and salary reviews.
- Ensuring that individuals are energized and motivated by their work.
- Organizing training management.
- Solution architecture.
- Introduction of DevOps mindset.
- Implementation of .NET/Azure solutions / PoCs.
- Administration of Azure/Azure DevOps.
- Definition of QA procedures.

Chief Technology Officer

Tech World With Milan

I help engineers, managers, and architects build great careers, products, and teams.

My articles have been viewed 300M+ times with 400K+ followers on various platforms, including LinkedIn, Twitter, Substack, and Medium.

Join my free newsletter:

Author

First Principles Consultancy

I provide coaching services for engineering leaders and engineers, including interviewing, management, organizational structure, leading teams, and career development.

In particular:

- Architecture consulting: I help companies design robust, cloud-based solutions that grow efficiently. I do audits, design reviews, and on-demand troubleshooting.

- Leadership & management coaching: I guide new and seasoned tech leaders in building and managing high-performing teams. I also coach on delegation, feedback culture, conflict resolution, and more.

- Team mentorship & training: I provide customized training sessions on coding best practices, engineering standards, DevOps, and Agile methods and mentor teams on personal growth and collaboration.

- Organizational design & culture: I advise on setting up engineering structures, processes, and a culture that fosters openness, innovation, and continuous learning.

- Product strategy: I collaborate with product teams to clarify priorities, set achievable milestones, and align tech choices with business goals.

 - Workshops & Masterclasses: I host events on topics such as “Transitioning from Engineer to Leader,” “Cloud Architecture Fundamentals,” “Building a Resilient Engineering Culture”, and more.

Also, I serve as a fractional CTO or tech advisor for select companies.

📩 DM me for more details.

Engineering Leadership Coach

3MD Studio

We partner with businesses to transform their ideas into scalable, high-quality software solutions. Combining your industry expertise with our technical know-how, we build reliable, efficient products while you focus on growing your business. 

My role is:
- Recruiting and working with a talent network
- Line management of 10+ employees, stakeholder, and vendor management
- Overseeing product and service offerings
- Delivery leadership & project management of mission-critical projects
- Managing and maintaining relationships with C-Level Management
- Ensuring that the quality of delivery of projects stays on a high level
- Troubleshooting and problem management to ensure the desired outcome
- Cultivating a strong company and leadership culture
- Ensuring compliance with industry regulations
- Software Architecture, Cloud & Consulting topics

Interim CEO

Zühlke Group

Worked on different projects with various clients, from small startups to large international and Big Tech companies.

Accomplishments:

- Completed responsibilities as a thought leader with expertise in Software and solution architecture, .NET, Azure, and Cloud.
- Acquired a thorough understanding of the ecosystems and fundamental guiding principles of software development.
- Supported the Zühlke Community and promoted a productive workplace where team members inspire one another. shared use cases, frequently ran technical training, and generated materials to support ongoing professional development.
- Increased social media traffic and digital exposure by attending conferences and writing articles for LinkedIn, blogs, magazines, and portals.
- Offered customized mentoring for team members, sharing important industry insights to produce deeper comprehension and considerably increase overall skill efficiency while taking on projects.

Worked on customer projects as a Software/Solution Architect and team lead in distributed teams with Switzerland, Germany, Austria, the UK, and Singapore. My responsibilities include:

- Creating the IT architectures for cutting-edge, ground-breaking software solutions and giving all clients high-level strategic advice.
- Cloud architecture (Azure).
- DevOps.
- Requirements engineering.
- Code reviews.
- Different roles in internal organization.
- Coaching less experienced colleagues/mentoring.
- Conducting technical interviews.

Customer projects:

- Advanced speech recognition engine (FAANG company): Speech-to-text recognition engine in the Cloud. Tech stack: SAFe, .NET, Azure, Azure DevOps, WCF, SQL Server.

Principal Consultant

Accomplishments:

- Built and led a group-wide Azure and AWS teams (CoP) for the UK, Germany, Switzerland, Austria, Portugal, Bulgaria, Serbia, and Singapore with 170+ people, enabling regular meetings and knowledge exchange.

- Built and led a local Cloud topic group (CoP) for Serbia, Bulgaria, and Portugal with 150+ people, certified 20+ people for Azure and AWS, set it up from the ground up, and created group-wide Azure and AWS teams with 170+ people.

My responsibilities included:
- Defining group mission and goals.
- Defining collaboration and communication channels.
- Organizing topic group events and internal organization.
- Sharing knowledge internally and externally.
- Defining tools to be used for communication and knowledge sharing.
- Engaging in regular communication with team members and others.
- Driving Cloud topic towards capability.
- Maintain relations with other cloud capabilities on the group level.
- Job profile definition with HR.
- Searching for the right people.
- Conducting technical interviews.

Group Cloud Capability Lead

Accomplishments:

- Recieved an overall score of 3.6/4 from peers.
- Got good feedback overall.

People management:
- Helping people grow and progress in their careers;
- Regular 1:1 meetings;
- Coaching and mentoring;
- Performance appraisal and feedback;
- Regular, constructive performance feedback
- Evaluations, defining goals, OKR & SMART Objectives;
- Mediation and conflict resolution;
- Defining job profiles;

Team Lead / Engineering Manager

Accomplishments:
- Led a team of developers from the Belgrade side in a distributed setup with Switzerland colleagues.
- Enabled successful project distribution for a major client.
- Led Thought Leadership initiative for ZRS.
- Given multiple talks/workshops on different topics (architecture, cloud, testing, clean code, mentoring, goals, etc.).

Worked on customer projects as part of a distributed .NET DEV team with Switzerland and Singaporean colleagues. My responsibilities included:
- Team architect.
- Team lead (Beg).
- Requirement engineering.
- Code reviews.
- Coaching/mentoring.
- Software/solution architecture, design, testing and implementation.
- Conducting technical interviews.

Customer projects:
- Integrated investment advisory platform for relationship managers of a private bank. Tech stack: Javascript/TypeScript, C#/.NET, SQL Server, Entity Framework, Stored procedures, AngularX, RxJS, ngrx, Log4net, Jobbr. The biggest account and project in Zuhlke Group: 50+ people, 1mil+ LOC.

Lead Software Architect

Worked on customer projects as part of a distributed .NET DEV team with Switzerland and Singaporean colleagues. My responsibilities included:
- Software architecture, design, testing, and implementation.
- Preparing bids for projects from the technical side.
- Conducting technical interviews.

Customer projects:
- Insurance Portal for Corporate Customers. Tech stack: C#, ASP.NET MVC/Core/Web-API, Entity Framework, IdentityServer3, SQL Server Integration Services, RavenDB, Kendo UI, TypeScript, LESS, TFS. (November 2017.-June 2018.). 600k+ LOC.
- GUI for Media Processing System. Tech stack: WPF, MVVM, C#, Feature-Based, FluentAPI, MVVMLight, ReFit, Aspose, SourceAFIS. (June 2018. - September 2018.)

Expert Software Engineer

I operated as an independent software development expert, architect, mentor, trainer, and programmer. 

As I have done in previous roles, I’m assisting my international clients in modernizing their software development. I am at my best working with projects and teams that need a boost in collaboration, performance, and technology. 

Some examples of my work include:

- As an agile coach, I turn around organizations from traditional ways of working to Agile, Scrum, Kanban, and continuous delivery. 
- As a career coach, I guide engineers, engineering managers, tech leads, etc.
- As a Fractional CTO, I implement new technologies in organizations, such as software architectures, modern modeling tooling, RESTful services, APIs, C#/.NET, functional programming, and Cloud technologies, such as Azure and Azure DevOps.
- As an architecture coach, I help organizations restructure unmaintainable legacy code bases to new, flexible, and tested software architectures.
- As a software architect, I create front-end and back-end frameworks and code using HTML5/CCS3, Bootstrap, Angular, C#, .NET, ASP.NET MVC, Xamarin, and WPF.
- As a mentor and teacher, I present seminars and (in-house) training courses on a variety of topics such as  Agile, Scrum, Kanban, Software Development, Software Architecture, Microservices, Continuous Delivery, Design Patterns, Domain-Driven Design, Modeling and UML, Clean Code, .NET, and Testing.

Independent Consultant, Owner

Electric Power Industry of Serbia

Accomplishments:

I led an organization of 40+ people who worked on the development of different software solutions by using Microsoft and Java-based, as well as some legacy tech stacks. Some of the applications we implemented are:

• Vehicle maintenance management system (Web - 100+ users).
• Procurement solution includes elements of the plan to implement procurement, contracts, etc. (Web - 500+ users).
• Project management team member for SAP ERP implementation, acting as an internal auditor (June 2014.  - December 2015.).
• Oracle UCM - document management system (support lead).

I was responsible for:
• Leading main software implementation projects.
• People management of 40+ people.
• Helping people grow and progress in their careers 
• Regular, constructive performance feedback
• Harmonising engineering processes, driving consistent standards and approaches throughout the team.
• Product development.
• Keeping the team updated with the latest software development technologies and methodologies.
• Coaching the team on agile software development best practices. 
• Developing short and long-term strategies for the team to meet the company's goals and objectives. Planning and monitoring the successful implementation of this strategy.
• Teaching and training software developers, organizing courses, etc.
• Overseeing internal product development.
• Supervising other software projects.
• Defining implementation times and deadlines (for development and support).
• Leading technical support.
• Defining new internal organizational structure.
• Internal staff recruiting.
• Planning and tracking procurements for a department.
• Management of the commission for the company's inventory of concessions, intellectual property, and software.
• I administered and configured the SQL Server 2008/2014 (backup, migrations, maintenance), Team Foundation Server, and Application Server (IIS).
• Management of company's internal portal and website.

Head of Engineering

Built and led a department from the ground up, hiring people, setting up processes, technology, etc.

Software architect and a team leader for software engineers developing ERP-based applications using .NET stack and also:

• Built a team of developers and business analysts from 0 to 10+ people.
• Defined standards, tools and processes that were used for solution development within company.
• Line management.
• Teaching and training software developers, organizing courses, etc.
• Design of the solutions architecture.
• Framework design.
• Product development management.
• Defining new internal organizational structure.
• Internal staff recruiting.
• Scrum Master.

Implemented projects by using .NET technology stack: 
• Materials management (Web - 400+ users)
• Asset management (Web - 300+ users)
• Invoicing (Web - 10+ users)

Head of Software Development Department

Senior software engineer and a team leader for following implemented projects (.NET stack):
• Storehouse calculation distributed application (Desktop), architecture and implementation (ASP.NET + C# + TFS + MS SQL Server).
• SQL Server Integration Services-based data migration from legacy system.
•  Administration and configuration of SQL Server 2008 (backup, migrations, maintenance), Team Foundation Server and IIS.
• Creation of procedures for data backup, programming style, user requirements form, etc.

Senior Software Engineer and Team Leader

Software developer for following projects (Java stack):
• Invoicing (Eclipse + EJB3 + Hibernate + Spring framework + Acegi Security + Struts 2 + JUnit 4 + JMock + JFreeChart + JasperReports + SVN + JIRA + Patterns).
• ArchiDoc Web Document Management System (Netbeans + EJB2 + Struts + JSP + JUnit 3 + Patterns).
• Other projects ( CMS for Web portal & CD presentation, E-commerce Web site implementation in PHP/MySQL,...) .
 • Teaching and training software developers, organizing courses, etc. Held Java course for 40 software developers (Basic, Advanced, Web, J2EE). Duration: one year.
 • Consultant for various Java projects.

Software Engineer

GOOD OLD AI Research Network

- I worked as a researcher during post-graduate studies in modeling and metamodeling, model transformations, rule languages, and business models. 

- Published 20+ papers in international conferences and journals and received awards for best papers. 

- Involved in international conferences and journals as an editorial board member, program chair, reviewer, and program committee member. 

- Cited 450+ times.

- Google Scholar h-index: 11.

- Member of ACM, the IEEE Computer Society, and GOAI Research group.

Researcher

Abell Soft

Implemented projects:
• E-Store application for on-line selling of books in PHP/MySQL. 
 • Complete code and design of a business packages FinanceBusiness for small and medium companies in Microsoft Visual C++, using MFC, Win32, PHP and MS Access (DAO, ODBC). 
 • Coded, designed and managed multiple web sites using HTML, JavaScript, PHP, MySQL, visual editors like Macromedia Dreamweaver and Microsoft FrontPage so as other graphical tools. 
 • Active IT System consulting for large number of companies.

Junior Software Developer

Municipality of Lajkovac

System Administrator

Chief Roadblock Remover and Learning Enabler | Software Development Expert | Leadership & Career Coach | Speaker | Author | Building great products, building great teams!

🎯 I'm a result-driven 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗟𝗲𝗮𝗱𝗲𝗿, 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗘𝘅𝗽𝗲𝗿𝘁, 𝗖𝗼𝗮𝗰𝗵, 𝗧𝗿𝗮𝗶𝗻𝗲𝗿 𝗮𝗻𝗱 𝗖𝗼𝗻𝘀𝘂𝗹𝘁𝗮𝗻𝘁 with 20 years of professional experience in different domains, including the 𝗳𝗶𝗻𝗮𝗻𝗰𝗲 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆, 𝗯𝗮𝗻𝗸𝗶𝗻𝗴, 𝗲𝗻𝗲𝗿𝗴𝘆 𝘀𝗲𝗰𝘁𝗼𝗿, 𝘁𝗿𝗮𝗻𝘀𝗽𝗼𝗿𝘁𝗮𝘁𝗶𝗼𝗻, 𝗶𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝘁𝗲𝗹𝗰𝗼. 

🚀 My mission is to provide 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗲𝘅𝗰𝗲𝗹𝗹𝗲𝗻𝗰𝗲 focused on architecture, the best agile practices, passionately 𝗯𝘂𝗶𝗹𝗱 𝗵𝗶𝗴𝗵-𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝘁𝗲𝗮𝗺𝘀 𝗮𝗻𝗱 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 to deliver innovative, 𝗵𝗶𝗴𝗵-𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀, working closely with customers and developing software products on different platforms.

🎓 As a 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝗿, I published 20+ papers and book chapters in international conferences and journals (having more than 440+ citations and h-index 11 on Google Scholar), obtaining 𝗣𝗵.𝗗. 𝗶𝗻 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗦𝗰𝗶𝗲𝗻𝗰𝗲𝘀. Also, I have been involved as a 𝗿𝗲𝘃𝗶𝗲𝘄𝗲𝗿 𝗮𝗻𝗱 𝗽𝗿𝗼𝗴𝗿𝗮𝗺 𝗰𝗼𝗺𝗺𝗶𝘁𝘁𝗲𝗲 𝗺𝗲𝗺𝗯𝗲𝗿—member of ACM, the IEEE Computer Society, and GOAI Research group. 

🧑‍💼 I work as a professional 𝗛𝗶𝗴𝗵-𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗖𝗮𝗿𝗲𝗲𝗿 𝗰𝗼𝗮𝗰𝗵 for people in engineering leadership positions.

✍🏻 Additionally, I am a 𝗽𝗮𝘀𝘀𝗶𝗼𝗻𝗮𝘁𝗲 𝗮𝘂𝘁𝗵𝗼𝗿, who helps more than 400,000 engineers, managers, and architects to build great careers, products, and teams through various platforms and channels.

𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲:

📌 Leadership
📌 Team building
📌 Making things efficient
📌 Creating self-contained systems
📌 Be highly organized and make others too
📌 Focus on strategy/goals
📌 Make things happen
📌 Career Coaching
📌 Mentoring
📌 Line management
📌 Effective client communication
📌 Software Architecture
📌 DevOps mindset
📌 Cloud (Azure)
📌 .NET/C# (ASP.NET/WPF)
📌 SQL Server/NoSQL
📌 Agile methods (Scrum, Kanban, SAFe)
📌 Product/project management

📲 Feel free to contact me here via LinkedIn (DM)!

Dr Milan Milanović

LinkedIn today - everyone is a 25-year-old senior software architect

We have people with 0-1 year of experience who write about software engineering, architecture, and more.

They have yet to see or work with most of this stuff.

I started writing with 20 years of experience in roles ranging from software engineer to architect and consultant, 30+ projects, and a Ph.D. in Computer Science. Still, I was unsure if my writing would be valuable enough for readers.

When you start following someone, check their credibility outside LinkedIn and other social networks first. Do they have the credibility to talk about this? 

I know this because many people copied my work, style, and topics. The latter is harder to catch, but it is wrong. This is not the right address to follow if someone doesn't talk from their own experience.

LinkedIn is not real life! It is an abstraction, and it can be very elusive.

Chief Roadblock Remover and Learning Enabler | Helping 400K+ engineers and leaders grow through better software, teams & careers | Author | Speaker | Leadership & Career Coach

Don't worry. There will be a place for everyone.

Learn AI and use it as your leverage to improve your work.

𝗕𝗼𝗼𝗸𝘀 𝗘𝘃𝗲𝗿𝘆 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗠𝘂𝘀𝘁 𝗥𝗲𝗮𝗱 𝗶𝗻 𝟮𝟬𝟮𝟱.

You can learn from experts by working with them or by reading their work. Books scale better.

Here are 30 books organized by what they'll help you master.

𝟭. 𝗚𝗲𝗻𝗲𝗿𝗮𝗹:
🔹 The Pragmatic Programmer by David Thomas and Andrew Hunt
🔹 Modern Software Engineering by David Farley
🔹 Code Complete by Steve McConnell
🔹 Grokking Simplicity by Eric Normand
🔹 Software Engineering at Google by Titus Winters

𝟮. 𝗗𝗮𝘁𝗮 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀 𝗮𝗻𝗱 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀:
🔹 Grokking Algorithms by Aditya Bhargava
🔹 Algorithms by Sanjoy Dasgupta
🔹 The Art of Computer Programming by Donald Knuth
🔹 Data Structures and Algorithms Made Easy by Narasimha Karumanchi
🔹 Introduction to Algorithms by Cormen, Leiserson, Rivest, and Stein

𝟯. 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝘀𝘆𝘀𝘁𝗲𝗺𝘀:
🔹 Designing Distributed Systems by Brendan Burns
🔹 Designing Data-Intensive Applications by Martin Kleppmann
🔹 Distributed Systems by Maarten van Steen and Andrew Tanenbaum
🔹 Distributed Algorithms by Nancy Lynch

𝟰. 𝗕𝗲𝘀𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀:
🔹 Clean Code by Robert C. Martin
🔹 Design Patterns by Gang of Four
🔹 Refactoring by Martin Fowler
🔹 The Effective Engineer by Edmond Lau
🔹 The Pragmatic Programmer by David Thomas and Andrew Hunt

𝟱. 𝗗𝗮𝘁𝗮:
🔹 Data Modeling Essentials by Graeme Simsion
🔹 Learning SQL by Alan Beaulieu
🔹 Fundamentals of Data Engineering by Joe Reis and Matt Housley

𝟲. 𝗗𝗲𝘃𝗢𝗽𝘀:
🔹 The DevOps Handbook by Gene Kim
🔹 Continuous Delivery by Jez Humble and David Farley
🔹 Accelerate by Nicole Forsgren
🔹 Effective DevOps by Jennifer Davis and Ryn Daniels

𝟳. 𝗧𝗲𝘀𝘁𝗶𝗻𝗴:
🔹 Growing Object-Oriented Software, Guided by Tests by Steve Freeman and Nat Pryce
🔹 Unit Testing by Vladimir Khorikov
🔹 Unit Testing Principles, Practices, and Patterns by Vladimir Khorikov
🔹 Test Driven Development by Kent Beck
🔹 Working Effectively with Legacy Code by Michael Feathers

𝟴. 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲:
🔹 Software Architecture Patterns by Mark Richards
🔹 Clean Architecture by Robert C. Martin
🔹 Software Architecture: The Hard Parts by Neal Ford and Mark Richards
🔹 Building Microservices by Sam Newman
🔹 Domain-Driven Design by Eric Evans

𝟵. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗔𝗜:
🔹 The Hundred-Page Machine Learning Book by Andriy Burkov
🔹 Designing Machine Learning Systems by Chip Huyen
🔹 AI Engineering by Chip Huyen

𝟭𝟬. 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽:
🔹 The Five Dysfunctions of a Team by Patrick Lencioni
🔹 Drive by Daniel Pink
🔹 The Making of a Manager by Julie Zhuo
🔹 Radical Candor by Kim Scott

𝟭𝟭. 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁:
🔹 How to Win Friends & Influence People by Dale Carnegie
🔹 Deep Work by Cal Newport
🔹 The Effective Executive by Peter Drucker

👉 Subscribe to my newslettter to get curated 100+ Books That Changed My Life mini-book: https://lnkd.in/dnmKdmRp

Having access to smarter people is a blessing, not a threat

Early in my career, I used to feel uncomfortable when surrounded by people who knew more than me.

I worried it exposed my gaps.

But over the years, I’ve learned the opposite is true:
Being surrounded by smarter people is the fastest way to grow.

Here’s why:

𝟭. 𝗧𝗵𝗲𝘆 𝗿𝗮𝗶𝘀𝗲 𝘆𝗼𝘂𝗿 𝗯𝗮𝗿.

You see new standards of thinking, execution, and leadership.

𝟮. 𝗬𝗼𝘂 𝗹𝗲𝗮𝗿𝗻 𝗳𝗮𝘀𝘁𝗲𝗿.

A single conversation can save you weeks of trial and error.

𝟯. 𝗬𝗼𝘂 𝗯𝗿𝗲𝗮𝗸 𝗹𝗶𝗺𝗶𝘁𝗶𝗻𝗴 𝗯𝗲𝗹𝗶𝗲𝗳𝘀.

When you watch someone solve problems you thought were “impossible,” your own ceiling shifts.

𝟰. 𝗬𝗼𝘂 𝗱𝗲𝘃𝗲𝗹𝗼𝗽 𝗵𝘂𝗺𝗶𝗹𝗶𝘁𝘆.

Growth requires admitting what you don’t know—and being willing to learn.

The real threat isn’t being the least experienced person in the room.
The real threat is staying in rooms where you’re the smartest.

So don’t shy away from people who outshine you.
Seek them out.
Learn from them.
And then, pass that knowledge on.

 👉 Who’s someone smarter than you who shaped your growth?

𝗢𝗻𝗲 𝗦𝗲𝗰𝗿𝗲𝘁 𝘁𝗼 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗚𝗿𝗲𝗮𝘁 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿

We've all heard the saying, "Practice makes perfect." But when it comes to software engineering, the real growth happens outside of the 9-to-5 grind. 

You can become a good software engineer through daily work assignments and on-the-job experience. 

However, the path to true greatness lies in what you do after work hours.

The harsh reality is that workplace projects often involve constraints, deadlines, and compromises (read Scrum and Jira :). 

You may only sometimes have the luxury to explore cutting-edge technologies, experiment with novel approaches, or dive deep into complex problem-solving. 

This is where your projects and self-driven learning come into play.

After work, you can stretch your skills, explore new territories, and tackle challenges that challenge your curiosity. 

It's an opportunity to learn at your own pace, make mistakes without consequences, and push the boundaries.

So, if you aspire to become a great software engineer, don't limit your growth to the office walls. 

Dedicating time after work to personal projects, contributing to open-source initiatives, attending meetups or conferences, or playing with new languages or libraries is crucial.

Do you agree?

Greg Coquillo

Amazon Web Services (AWS)

As a member of the Data Center Systems & Automation (DCSA) Critical Operations Leadership team, I lead a cross-functional groups comprised of 100+ Network Engineers, Software Engineers, Technical Infrastructure Program Managers, and Infrastructure Delivery to build robust distributed systems (using Native AWS services) that accelerate network scaling capacity for AWS customers to support global AI/ML workloads.

Product Management Technical - Data Center Automation

Amazon

Owner of the AI vision and roadmap for Private Brand’s Compliance Division. Driving adoption of new technology, allowing my customers to scale their processes and increase productivity.

While at Amazon Retail, I led cross-functional teams of Product, Engineering Program, and Risk to innovate on behalf of customers, while ensuring product compliance and safety. The collective effort reduced time-to-market by xx% and operational costs by $xxM annually.

Deliverables include: 
-A Transformer-based document orchestrator and processor handling xxxM data points per month.

- An automated purchase order gate that increased Gross Merchandise Sales by $xxxM.

 -A successful migration of xx petabytes of data to a new, secure, and private data lake with Redshift-enabled architecture to ensure our business complies with global regulations.

Product Management, Gates & Media Automation

CharlieDAO

CharlieDAO is a collective of analysts, product folks, crypto natives, and web developers making Web 3.0 tools and systems. Areas of Focus: NFT Analytics & DeFi primitives. 

We're growing on Discord, please DM if you would like to learn more and possibly join! All we expect is a willingness to learn & participate 2-5 hours / month. Sharing articles, meeting others, and supporting projects as you see fit.

Advisor - Product Management & Strategy

Lonza

Harvesting the power of information through global data analysis, professional networking, cross-team collaborations and strategic business decisions to maximize profits for the $1.2B Microbial Control Solutions Portfolio.

Lead Product Manager, Global Pricing Optimization

Maximize Profit (EBITDA) for a global portfolio of products achieving $350 Million in annual sales.
•	Improve MRP system via SAP, BW and IBP for global sites’ Raw Materials and Finished Goods to enable automated replenishment, production planning and service delivery. 
•	Implement and lead Value Chain and S&OP meetings globally and set KPIs for all sites.  
•	Manage product’s life cycles through proper segmentation and value category.
•	Analyze value chain and submit Capex for Key Projects to increase market share and improve operational efficiency
•	Post-Merger integration and portfolio optimization between Lonza and Diacon.  Reduce portfolio by 25% by eliminating custom blends and re-branding under Lonza umbrella. 
•	Partner with Sourcing, Supply Chain, Operation, Marketing, Sales and IT globally to bring value to the organization by organic market share gain and operational excellence.

Lead Product Manager, Global Value Chain

Avery Dennison

Lead a team of 60 employees and drove profitability for an $80 Million business.
- P&L Management and Owner of key KPI's including Safety, Quality, Productivity, Cost and Delivery for the Finishing Department.
-Lead deployment of continuous improvement activities centered on data-driven root cause analysis, Corrective Actions and Counter Measures. 
-Managed the department's R&O (Risks and Opportunities), while aligning with Divisional S&OP and reducing Labor Hour per BSI by 30 % in 6 months.

Value Stream Manager

Supporting key plant metrics to contribute to the organization’s OGSM:
			Safety - Zero Recordable (Zero is Achievable Program)
			Quality - CPMO (Complaints per Million Opportunities) target of 1.37
			Delivery - Stock Flex target 97.3%, Custom Coat target 97%
			Productivity - Coater UEE (Uptime and Equipment Effectiveness 70%)
			Losses (% of TVP) - Target of 2.22%
•	Manage the Safety Participation Program to achieve 100% completion rate
•	Support the plant’s ISO 9001 standard through the management of the Quality Inspection System (Technical Analysis, Certificate of Analysis, Process Control Standards, etc…)
•	Manage the Corrective and Prevention Action Program for customer satisfaction
•	Establish rapport with vendors and partner with them during Continuous Improvement projects
•	Support customers claims with short response time while providing root cause analysis and corrective action
•	File Vendor Claims exceeding $400,000 per year to reduce Losses
•	Lead the Coater Wrinkle Project to reduce wrinkles claims by 25%
•	Lead the Finishing Machine Cleaning Standard Project to reduce contamination claim by 50%
•	Perform data analysis of adhesive coat weight profiles using an ROQ Roll Hardness Tester to predict  die change (Total Preventive Maintenance) and avoid High/Low Coat Weight claims

Quality and Safety Process Engineer

Global Operations Leadership Development Associate
Worked with plant leaders and cross-functional team members of Finishing, Coating, Shipping and Supply Chain to achieve a stock delivery flexibility of 97.6% and a custom coat shipped flexibility of 98% while leading a team of 28 employees in Finishing and driving operational excellence through safety, quality, delivery and cost. 
•	Zero safety injuries in 2014 through daily improvements and communications using our “Zero is Achievable” approach. 
•	Reduced transactional quality complaints by 30% with the B-Shift Finishing team.
•	Increased productivity by 15% through SMED and flexible staffing.
•	Increased training completion rates and overall skills of B-shift Finishing (Operators, Assistants).
•	Consolidated small rolls and reduced by 25% in the facility and increased machine up time by 10%

Finishing Manager

Global Operations Leadership Development Associate
Collaborated with plant leaders including the materials manager, the master production scheduler and coating manager to improve and maintain a service level of 99.3%, a custom coat shipped flexibility of more than 90%, and a stock delivered flexibility of 94%, while saving at least $400,000 in operational costs across LPM plants and promoting safety through the “Zero is Achievable” campaign.

Supply Chain Planner

Publix Super Markets

Teamed up with manufacturing leaders like plant managers, production and quality control managers, plant engineers and production line associates to execute continuous improvement projects that included waste reduction, quality assurance, safety training, transfer of knowledge and leadership development, expecting to save upwards of $3,000,000 for 2 Bakeries, 2 Fresh Foods, 1 Deli plants by the 4th quarter of 2011.

Industrial Process Engineer

AI Product @Amazon | Startup Investor | 2X Linkedin Top Voice - 2021 Tech & Innovation, 2020 Data Science & AI | Quantum Computing & Web 3.0 | I use technology to scale and improve compliance processes | Content Creator

Book a session with at Topmate: https://topmate.io/greg_coquillo

Product leader with a passion for solving business use cases such as Growth, Go-to-Market, and Price Optimization. I owned P&L for commodity-based products, and leveraged AI to increase the average customer contract values across business industries. As a Product Leader at Amazon, I own a roadmap that drives the adoption of AI products through the Media Automation charter, which supports Private Brands’ YOY growth. I work with Data Scientists, Software Engineers and Business Intelligence Engineers to leverage Machine Learning and Big Data Analytics to design predictive models that enhance business decision-making and performance to accelerate growth, reduce Time to Market of products, monitor cross-geo compliance and import requirements, and ensure customer safety.

I am always curious and eager to learn how new technology affects our daily lives, and I love to support communities on their quest to adopt Artificial Intelligence across industries. Feel free to contact me if you have Data Science projects that you’d like to be showcased on this platform for visibility.

🌐I am a Global Business Leader with 12 years of experience and skills including Product Management, P&L management, Global Networking, EBITDA Optimization, Pricing Management, Margin Management, Product Management, Program Management, Operations and Supply Chain Management, Lean Manufacturing, Six Sigma, and Continuous Improvement.

🧘🏽‍♂️Soft Skills include: Communication, Collaboration, Lateral Thinking, Adaptability, Inspiration, Innovation, Empathy, Listening, Diplomacy, Organization, Responsibility, Leadership...

🎬🎥🎞 I really enjoy watching movies, and if I could redo it all over, I’d be a Sci-Fi script writer so I can be bounded by the limits of imagination.

(Opinions are my own)

Product Management, Media Automation

Owner of the AI vision and roadmap for Private Brand’s Compliance Division. Driving adoption of new technology, allowing my customers to scale their processes and increase productivity. Core responsibilities include:

-Building cross-functional teams consisting of Data Scientists, Software Engineers, Data Engineers, Product Managers and Program Managers, to plan and implement sophisticated AI projects
-Manage the New Initiative Intake Process
-Gather Business requirement artifacts
-Communicate roadmap definition and prioritization
-Coordinate User Acceptance Testing
-Manage Launch Announcements
-Prepare and communicate annual planning artifacts

GenAI vs AI Agents vs Agentic AI; What’s the Difference?

AI is moving faster than ever, but each type plays a unique role. GenAI focuses on creativity, AI Agents drive automation, while Agentic AI enables true autonomy in workflows.

From instant content generation to multi-step reasoning and decision-making, this evolution is redefining how businesses innovate, scale processes, and unlock new possibilities with AI.

Here’s a quick breakdown:

- 🔹GenAI: Generates outputs like text, images, and code. It’s instant, prompt-driven, and great for single-task execution (think ChatGPT, MidJourney, DALL·E).

- 🔹AI Agents: Go beyond outputs to automate workflows and actions. They connect with APIs, trigger events, monitor performance, and execute across apps (Zapier AI, n8n, Make.com).

- 🔹Agentic AI: The next leap. These systems think, plan, and act across multiple steps. They reason strategically, store context, dynamically call tools, and run deterministic workflows (LangChain, ReAct, CrewAI).

In short:

- GenAI = Creates
- AI Agents = Automates
- Agentic AI = Thinks + Acts

What differences do you see?

#AI

Product Leader @AWS | Startup Investor | 2X Linkedin Top Voice for AI, Data Science, Tech, and Innovation | Quantum Computing & Web 3.0 | I build software that scales AI/ML Network infrastructure

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🗺️ Here’s a quick mindmap to learn Agentic AI

Agentic AI is so far the biggest shift from traditional, static AI models to autonomous, goal-driven systems capable of reasoning, planning, and acting on their own. 

Instead of just generating text or images, these agents can now make decisions, interact with tools, and even collaborate with other agents. And they do so all while learning continuously.

If you’ve ever wondered how ChatGPT plugins, AI copilots, or workflow automation tools “think,” that’s Agentic AI in action. Learning it means understanding how to design agents that plan, execute, and optimize tasks dynamically, bringing human-like adaptability into automation.

1. Agentic AI Fundamentals
Start with the basics, which is “what Agentic AI is”, how it differs from traditional AI, and the types of agents (reactive, proactive, goal-driven). Learn how agents communicate with humans or other agents to complete complex workflows.

2. Querying and Interaction
Master prompt engineering for agents, context management, and natural language interfaces. Learn how agents handle multi-turn conversations and execute actions using APIs or databases.

3. Core Components & Architectures
Understand the building blocks of Agentic AI which include frameworks (LangChain, AutoGen, CrewAI), reasoning methods like Chain-of-Thought and Tree-of-Thought, and memory systems that retain and use information efficiently.

4. Optimization and Scaling
Explore how agents coordinate in multi-agent systems (agent swarms), decompose complex tasks, and optimize workflows through feedback loops and continuous learning.

5. Tools and Ecosystem
Get hands-on with popular frameworks like LangChain, AutoGen, Haystack, and CrewAI, and connect them to LLMs such as GPT-4 or Gemini. Learn how to use APIs, plugins, and orchestration tools like Zapier or Make for real-world deployment.

6. Security, Safety, and Reliability
Building responsibly involves implementing role-based permissions, policy enforcement, human-in-the-loop supervision, and strategies for error recovery and reliability in agent systems.

7. Use Cases and Applications
Apply what you’ve learned to real-world solutions: customer support agents, workflow automation assistants, data analysis copilots, or team collaboration bots.

🔹Here’s a tip for you: Start small by building a single-task agent, then expand into multi-agent collaboration. Continuous iterations over time will improve your agentic system building skills.

#agenticsystems

Check out this 8-Layer Architecture for LLM Systems

Large Language Models (LLMs) are more than just massive neural networks, they’re complex multi-layered systems built for performance, reliability, and scalability.

Each layer plays a unique role; from managing raw data and embeddings to deployment and safety. Together, they form the backbone of how modern AI operates in real-world environments.

1. Infrastructure Layer
The foundation of LLMs, handling compute power, networking, and storage across CPUs, GPUs, or TPUs.

2. Data Processing Layer
Focuses on data ingestion, cleaning, tokenization, and sampling, which turns raw data into training-ready datasets.

3. Embedding & Representation Layer
Transforms words into numerical embeddings for semantic understanding using techniques like positional encoding and PCA.

4. Model Architecture Layer
Defines the core neural network structure which includes attention heads, normalization, and architecture design for token prediction.

5. Training & Optimization Layer
Handles pretraining, fine-tuning, and distributed optimization for model performance and scalability across datasets.

6. Alignment & Safety Layer
Ensures models align with human values and ethics through reinforcement learning, feedback loops, and safety policies.

7. Evaluation & Serving Layer
Manages testing, inference, and model evaluation pipelines, ensuring reliability and real-world performance consistency.

8. Deployment & Integration Layer
Covers API deployment, SDKs, monitoring, and analytics, bringing the model into production environments.

To summarize, each layer in the LLM architecture contributes to a balanced system that enables real-world integration. However, this doesn’t come without challenges.


#LLM

Agentic AI is not built overnight, it’s structured like a pyramid, where each layer adds new skills, controls, and intelligence. At the base, we have core AI capabilities, and as we climb upward, we reach goal-setting, collaboration, governance, and full autonomy.

Here’s how the pyramid unfolds:

🔹 Foundation Layer: Core AI Capabilities
This is the base, core machine intelligence skills. It covers natural language understanding, test generation, contextual coherence, conversation handling, and pattern recognition.

🔹 Knowledge Layer: Context & Memory
Adds memory and reasoning abilities - RAG for retrieval, access to structured/unstructured data, short-term/long-term retention, and domain expertise.

🔹 Planning Layer: Goal Orientation
Enables structured thinking. AI learns breaking tasks into steps, prioritization, multi-step execution, adjusting plans, and building efficiency.

🔹 Tool Layer: Action Execution
Focuses on interacting with the real world - API calls, integrating third-party apps, automating workflows, and running tools to complete tasks.

🔹 Collaboration Layer: Multi-Agent Systems
Agents learn to work together. This includes role specialization (researcher, executor, verifier), communication, coordination, and shared knowledge exchange.

🔹 Governance Layer: Safety & Control
Critical for trust - adds guardrails, bias/risk mitigation, human-in-the-loop oversight, auditability, and ethical reasoning.

🔹 Apex Layer: Autonomy & Adaptability
The highest level. Agents become self-directed, adapt to dynamic environments, continuously learn, and handle uncertainty independently.

In short: The Agentic AI Pyramid shows the path from raw intelligence → memory → planning → action → collaboration → governance → full autonomy. Each layer builds on the previous one, ensuring AI grows from capable to trustworthy and adaptable.

#AgenticAI

If you look closely at this stack across providers, you’ll notice that AI is just part of the puzzle. I’m not exaggerating when I say, when launching production-grade systems, 80% of the AI challenges continue to be engineering challenges.
 
Selecting which model to work with isn’t even close to being the whole story. To successfully deploy and scale intelligent systems, one needs to understand how to make tradeoffs while evaluating hundreds of services offered by cloud providers like AWS, Google Cloud, and Microsoft Azure 

Each cloud has its edge; AWS leads in scalability, Google in data innovation, and Microsoft in enterprise integration. Let’s see how they compare across every key layer of the stack :

1.🔸Security & Governance
- AWS ensures secure access and monitoring with IAM and GuardDuty.
- Google focuses on unified security through Command Center and KMS.
- Microsoft leads enterprise defense with Azure Defender and Sentinel.

2.🔸Integration & Automation
- AWS automates workflows with Step Functions and Glue.
- Google connects systems using Dataflow and Workflows.
- Microsoft streamlines operations through Logic Apps and Data Factory.

3.🔸Compute & Infrastructure
- AWS delivers scalable compute with EC2, Lambda, and Inferentia chips.
- Google uses TPUs and GKE for AI scalability.
- Microsoft powers hybrid workloads with Azure VMs and Functions.

4.🔸Data & Analytics
- AWS supports data analysis through Redshift and Athena.
- Google dominates big data with BigQuery and Looker.
- Microsoft combines analytics and visualization via Synapse and Power BI.

5.🔸Edge & Hybrid
- AWS offers low-latency AI with Outposts and Wavelength.
- Google secures edge processing with GDC and Confidential Computing.
- Microsoft extends cloud capabilities using Azure Arc and Stack Edge.

6.🔸Cloud AI Services
- AWS offers SageMaker, Comprehend, and Rekognition APIs.
- Google provides Vertex AI and Gemini for advanced AI solutions.
- Microsoft integrates OpenAI, Cognitive Services, and ML Studio.

7.🔸Agent & Developer Tools
- AWS includes Bedrock Agents and CodeWhisperer.
- Google enables Gemini and LangChain integrations.
- Microsoft supports Copilot Studio and Semantic Kernel.

8.🔸Prototyping & Design Tools
- AWS empowers testing with SageMaker Studio Lab.
- Google simplifies development using AI Studio and Opal.
- Microsoft focuses on no-code creation via Designer and Recognizer Studio.

9.🔸Core Models
- AWS relies on Titan and Bedrock models.
- Google leads with Gemini.
- Microsoft uses Phi, Orca, and Azure OpenAI.

Understand how to set up your architecture for scalability, performance, cost, and reliability is a huge advantage, whether via single-cloud, multi-cloud, hybrid, or on-prem. 

Curious to know how you evaluate tradeoffs from services across these providers to set up your AI systems.

Ever tried to connect the dots between AI, ML, DL, and LLMs and felt like you were swimming in a sea of acronyms?

AI Engineers who consistently build AI products have learned that understanding the full AI ecosystem is just as critical as knowing the latest model release. One cannot successfully build intelligent systems without a fundamental understanding of the layers that make them work, from data and models to infrastructure and agents.

This All in One AI Cheat Sheet (2026 Edition) is a compact technical map that shows how everything fits together across the modern AI stack.

Here is what it covers:

1️⃣AI vs ML vs DL
Learn how Artificial Intelligence, Machine Learning, and Deep Learning differ and where they converge.

2️⃣Core Concepts
Grasp the fundamentals of neural networks, supervised and unsupervised learning, and reinforcement learning.

3️⃣Model Types
Explore regression, classification, clustering, generative models, and transformers that enable context aware reasoning.

4️⃣AI Tools and Frameworks
Work with TensorFlow, PyTorch, Scikit learn, LangChain, AutoGen, and CrewAI to build and deploy intelligent systems.

5️⃣Popular Algorithms
Understand the mathematical foundations behind Linear Regression, Random Forests, SVM, and Gradient Boosting.

6️⃣AI Infrastructure
Discover how FAISS and Pinecone enable vector storage, while MLflow and Weights and Biases support experiment tracking and monitoring.

7️⃣Agentic AI
My favorite evolution in the AI landscape. These are systems that plan, reason, and act autonomously using protocols such as MCP (Model Context Protocol) and A2A (Agent to Agent Communication). This is where LLMs evolve into agents that can collaborate and learn continuously.

8️⃣LLM Techniques and Prompting
Master methods like Chain of Thought, ReAct, RAG, fine tuning, and tool use to enhance reasoning and adaptability.

9️⃣Evaluation Metrics
Track performance through accuracy, RMSE, BLEU, Perplexity, and autonomy efficiency to measure true model and agent impact.

🔟Real World Use Cases
See AI in action through predictive analytics, voice generation, robotics, and decision making in dynamic environments.

Would love feedback on anything to be added or removed from this list. Dont forget to share your experience with the community at large. 

#AI

Who’s leading the charge among the Top 15 AI Companies?

We’ve witnessed how AI has gradually become the backbone of value in tech. Check out how these top 15 major players structure their moats to deliver value in this space:

🔸NVIDIA ($4.33T) anchors the stack since its GPU and compute engines power nearly everything.

🔸Microsoft ($3.80T) and Apple ($3.79T) are turning AI into product abstractions at scale.

🔸Google / Alphabet ($2.98T) remains a core innovator via models, search, and foundation services.

🔸Meta ($1.86T) and Tesla ($1.46T) push AI into social systems, robotics, perception, and autonomy.

🔸Players like CoreWeave, Palantir, Adobe, and Tempus AI illustrate how infrastructure, domain specialization, and platform depth are increasingly prized.

Some Insights Worth Noting

🔹Infrastructure is shifting from commodity to strategic. The recent $14B Meta-CoreWeave deal underlines how long term compute supply contracts can alter competitive balance.  

🔹Among AI firms, multiples remain aggressive: the median revenue multiple is approx 25-30×.  

🔹Efficiency metrics matter: some “AI supernovas” deliver over $1.1M ARR per engineer, far above conventional SaaS norms.  

🔹The frontier is vertical AI: models fine-tuned for health, law, finance, biotech will win share over general models when domain constraints and compliance weigh heavily.

What does real moat look like? It’s a combination of compute + model IP + data + application + feedback loops.

Which company would you add to the list and which one would you remove? And why?

graphical user interface, application, Teams

We’re often fascinated when observing AI Agents act autonomously, being capable of planning, reasoning, and collaborating like humans. But how many of us are aware of what’s under the hood to make all of this possible?

Behind every powerful AI agent lies a multi-layered protocol stack, just like the internet has TCP/IP or cloud has architecture layers.

The below stack defines how agents think, act, and interact, enabling structured intelligence across reasoning, communication, execution, and learning.

Think of it as the “Operating System of Agentic AI”, where each layer plays a role in making decisions, retrieving knowledge, executing tasks, and adapting from feedback.

Here’s the breakdown:

🔸Foundation Layer (L1):
The core data and compute layer, powered by LLMs (GPT, Claude), embeddings, datasets, and GPUs/TPUs.

🔸Execution Layer (L2):
Runs the agent’s actions in real or simulated environments via runtimes like LangGraph and automations on Make.com or n8n.

🔸Infrastructure Layer (L3):
Ensures secure communication through context tokens, message passing, and protocols like A2A and A2E.

🔸Agent Layer (L4):
Focuses on autonomy and intelligence. Includes memory systems (LangChain Memory), tool usage (MCP, APIs), and feedback loops (RLHF).

🔸App Layer (L5–L7):
Handles interaction and coordination, which includes reasoning, planning, and knowledge access using tools like ReAct, ReWOO, and RAG.

In this complex space, building modular intelligence is a good strategy. As an example, you may not need a persistent memory for your agents at first launch, based on well thoughtout tradeoffs. Later on, you should be able to evolve your system by adding long-term memory. Just be careful about how this addition will affect user experience as well as agent performance. 


#AIAgents

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