Dobromir S.

Aiployees

Founder & Conversational AI Expert

NO Work Bulgaria

Co-Founder & AI Ambassador 

Linkbl

Founder & LinkedIn Growth Strategist

Smart IT Control

Partnership & Growth Lead

NoWork.ai

AI Ambassador 

British Bulgarian Business Association (BBBA)

Member

BESCO - The Bulgarian Startup Association

The TOP Person: An International Business, Politics, and NGO Magazine & a Global Initiative with HUB

Ambassador

Mercatus Outsourcing

-Design and implement business strategies, plans and procedures
-Set comprehensive goals for performance and growth
-Establish policies that promote company culture and vision
-Oversee daily operations of the company and the work of executives (IT, Marketing, Sales, Finance etc.)
-Lead employees to encourage maximum performance and dedication
-Evaluate performance by analyzing and interpreting data and metrics
-Write and submit reports to the CEO in all matters of importance
-Participate in expansion activities
-Manage relationships with partners/vendors

Chief Operating Officer

People Operations Business Partner

International Sales Director

-Maintain efficient relationship with all international customers
-Establish sales campaigns targeted at specific vertical and product markets.
-Monitor and recommend improvement to sales plans to achieve all key business objectives.
-Design sales contacts for all vertical markets.
-Provide new business setups and implement same for new product lines and ensure growth
-Design various innovative marketing strategies to increase sales for all product launches.
-Maintain all activities related to product marketing and provide support to sales and establish effective marketing communication.
-Develop and maintain good professional relationships with main accounts and strategic partners of company.
-Coordinate with marketing team to determine international marketing application and ensure compliance with company’s rule.
-Analyze and identify appropriate partner for each product in geographic market.
-Provide new business setups and implement same for new product lines and ensure growth.
-Provide and implement various company pricing policies.
-Administer sales pipeline and improve opportunities for sales.
-Assist global agents with technical backup, project implementation for all marketing strategies.
-Prepare monthly reports and provide and effective recap of previous year performance and review same.
-Develop and implement an operational plan for reservation and provide forecast to executive management.

International Sales Manager

Business Development Manager

The Linked Source

Co-Founder & COO

Why us?
At Aiployees, we bridge the gap between innovative AI solutions and practical business applications, helping organizations harness the transformative power of artificial intelligence.

🎯 Core Focus Areas:

Strategic implementation of Conversational AI solutions
AI product strategy and development guidance
Business growth optimization through AI integration
Partnership development in the AI ecosystem

💡 Professional Expertise:

Designing and deploating customized AI assistant solutions
Creating scalable AI implementation strategies
Developing ROI-focused AI adoption roadmaps
Building strategic partnerships in the AI industry

🔍 What I Bring to the Table:

Deep understanding of both business needs and AI capabilities
Proven track record in driving revenue through AI solutions
Expertise in optimizing customer experience through conversational AI
Strategic approach to AI integration and digital transformation

🤝 Current Focus:
I'm passionate about helping businesses navigate the complex landscape of AI implementation. My approach combines strategic business development with deep technical understanding, ensuring that AI solutions not only meet current needs but scale for future growth.

🌐 Looking to Connect:
I'm always eager to connect with fellow AI enthusiasts, business leaders, and potential partners who are interested in exploring the transformative potential of Conversational AI. Whether you're just starting your AI journey or looking to optimize existing solutions, let's discuss how we can innovate together.

📧 Connect with me at: dobromir@aiployees.com

Together, we can shape the future of AI-driven conversations and transform how businesses interact with their customers. 

Let's explore the possibilities! 🚀

Dr Emmanuel Ogungbemi

Head of Data Engineering | I help you improve your productivity with data and AI

From being a data analyst to leading the development of data pipelines, my journey in data engineering and analytics has been both challenging and rewarding.

When I began my career in data, I faced the daunting task of turning vast amounts of raw data into actionable insights.

The problem? Many organisations struggle with fragmented data and inefficient processes.

Here's how I tackled these challenges and built a successful career in data:

1. Crafted Data Solutions: Designed and implemented a Common Data Platform, ensuring seamless data integration and access.
2. Led Innovative Projects: At NATS, I contributed to the world's first AI system for air traffic control, utilising digital twinning and machine learning.
3. Developed Future Talent: Shaped data science and engineering education at Multiverse by developing comprehensive curricula for degree-level apprenticeships.
4. Consulted on High-Impact Projects: Provided AI and data science consultancy for industry giants like BP, Sword and Chaucer (now BIP UK), driving data-driven decision-making and innovation.

Now, I'd like to talk about you.

Are you aspiring to become a successful data analyst/data scientist/data engineer but need help figuring out where to start?

Here's a roadmap to guide you:

1. Master the Fundamentals:
- Learn key programming languages like Python and SQL.
- Understand data structures, algorithms, and database management.

2. Gain Practical Experience:
- Work on real-world projects to apply your skills.
- Use tools like Power BI and Tableau for data visualisation.

3. Join the Community:
- Participate in data science forums and attend industry events.
- Network with professionals to exchange knowledge and opportunities.

4. Continuous Learning:
- Stay updated with the latest trends and technologies in data analytics.
- Take advanced courses to deepen your expertise.

To help you get started, I've created a comprehensive Data Analyst Roadmap that outlines the essential steps and resources you need to succeed.

Join my email list to register your interest in my free Data Analyst Roadmap.

You have the potential to excel in the data industry, and I'm here to support your journey.

Let's connect and share our experiences in data engineering and analytics!

Head of Data Engineering

I am excited to share a comprehensive resource: Machine Learning, Deep Learning, and Artificial Intelligence Cheat Sheets compiled by Nikhil Yadav from Stanford University and MIT!

This collection of cheat sheets covers key concepts and techniques across machine learning, deep learning, and AI. 

Perfect for students, professionals, and enthusiasts, this document provides quick references and summaries of essential topics in these fields.

Key topics include:
- Probability and Statistics fundamentals
- Supervised and Unsupervised Learning algorithms
- Linear and Logistic Regression
- Support Vector Machines (SVM) and Generative Learning
- Tree-based and Ensemble Methods
- Principal Component Analysis (PCA) and other dimension reduction techniques
- Neural Networks, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs)
- Reinforcement Learning and Control
- Essential metrics for model evaluation and selection
- Tips and tricks for effective machine learning practice


Enhance your understanding and mastery of machine learning, deep learning, and AI with these detailed cheat sheets!

- Repost to share with others
- Save to revisit down the road
- Add your comment!

I help you break into data science and AI with practical tips, real-world insights, and the latest trends.

ML, Deep Learning and AI Cheat Sheet

I am excited to share an essential resource: Microsoft Azure Databricks for Data Engineering!

This comprehensive guide explores how to build production data pipelines with Apache® Spark™ in the cloud using Azure Databricks. 

It covers everything from data sources to end-user enablement, making it an invaluable resource for data engineers and businesses aiming to leverage big data for informed decision-making.

Key topics include:
- Data engineering tasks: ETL, data cleansing, and feature engineering
- Ensuring production readiness and compliance
- Scaling infrastructure to handle large volumes of structured and unstructured data
- Compatibility with various data stores and integration tools
- Benefits of Apache Spark, including performance, flexibility, and developer-centric features
- Detailed overview of Azure Databricks' features, such as autoscaling clusters, secure SQL server connections, and comprehensive API


Unlock the full potential of Azure Databricks for your data engineering projects and streamline your data workflows with this detailed guide!

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- Add your comment!

Microsoft Azure Databricks for data engineering

Excited to share an essential resource: DAX Query Plans by Alberto Ferrari!

This comprehensive paper introduces the performance analysis and optimization of DAX code through the use of DAX query plans. 

Ideal for BI professionals and data analysts, this document provides valuable insights into improving query performance by understanding and optimizing DAX queries.

Key topics include:
- Finding and interpreting DAX query plans
- Differences between logical and physical query plans
- Overview of the formula engine and storage engine
- Basic optimization techniques for DAX queries
- Practical examples using the Contoso and AdventureWorks databases


Enhance your DAX skills and improve query performance with this detailed guide!

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- Add your comment!

li_at

JSESSIONID

Talks about #data, #kindness, #interviewtips, #selfimprovement, and #careertransition

DAX Query Plans

Excited to share an essential resource: DevOps Interview Questions!

This comprehensive guide covers a wide range of questions that are commonly asked in DevOps interviews. 

Whether you're a beginner or an experienced professional, this document provides valuable insights and answers to help you ace your interview and enhance your DevOps knowledge.

Key topics include:
- Understanding DevOps and its need in the industry
- Top DevOps tools and their uses
- Differences between DevOps and Agile/SDLC
- Core benefits and advantages of DevOps
- Key performance indicators (KPIs) for gauging DevOps success
- Role of configuration management and popular tools like Ansible
- Concepts like "Shift left to reduce failure," Blue/Green Deployment, and more
- In-depth look at DevOps methodologies and real-life use cases
- Detailed explanations of tools like Docker, Kubernetes, Jenkins, and AWS


Enhance your interview preparation and stay ahead in the competitive DevOps field with this detailed guide!

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- Add your comment!

DevOps Interview Questions

I'm excited to share an essential resource: Python Notes/Cheat Sheet by Mark Graph!

This comprehensive cheat sheet covers a wide range of Python concepts, providing quick references and summaries for both beginners and experienced developers. 

It's an invaluable resource for anyone looking to enhance their Python programming skills.

Key topics include:
- Basic syntax and naming conventions
- Data types: integers, floats, strings, lists, tuples, sets, and dictionaries
- Operators and their functionalities
- Flow control statements: if, for, while, and try/except
- Functions and lambda expressions
- Modules and import statements
- Object-oriented programming: classes, methods, and inheritance
- Commonly used libraries and modules: datetime, math, re, numpy, pandas, matplotlib
- Advanced concepts: iterators, generators, and decorators


Enhance your Python programming knowledge and boost your productivity with this detailed cheat sheet!

- Repost to share with others
- Save to revisit down the road
- Add your comment!

Python Notes

Why data engineers are like the unsung heroes of the tech world – and why you should become one.

Ever wondered what makes the tech world tick?

It’s not just the flashy apps or cutting-edge AI.
It’s the data engineers behind the scenes, making everything work smoothly.

Here’s why they’re the real MVPs:

1. Foundation Builders

Data engineers lay the groundwork for any data-driven project.

Without them, data scientists and analysts would be flying blind.

They create robust data pipelines that ensure seamless data flow – the backbone of every tech solution.

2. Problem Solvers

Every day brings a new challenge.

From fragmented data sources to integrating complex systems, data engineers thrive on solving intricate puzzles.

Their work is crucial for turning raw data into actionable insights.

3. Innovation Enablers

The world’s first AI system for air traffic control?

That’s just one example.

Data engineers make groundbreaking innovations possible by ensuring data is clean, reliable, and ready for analysis.

4. Guardians of Data Integrity

Data is valuable, but only if it’s accurate.

Data engineers ensure quality and consistency, protecting organisations from costly errors.

They’re the gatekeepers of trustworthy data.

5. Educators and Mentors

Building the future doesn’t stop at technology.

Data engineers also shape the next generation of tech talent, sharing their knowledge and experience with aspiring professionals.

So, why should you consider becoming a data engineer?
→ It’s a role that combines creativity and technical skill.
→ There’s an evergrowing demand for data-driven solutions.
→ You’ll be at the forefront of innovation, where every project is an opportunity to make a significant impact.

Join the ranks of these unsung heroes and be part of a community that drives the tech world forward.

Are you ready to build the future?

Let’s discuss why data engineering should be your next step.

Drop a comment below to share your thoughts or ask questions about this exciting field.

Data degrees are great, but here’s why data engineering skills might just be the game-changer you need.

In a world overflowing with data, what’s the biggest challenge?

Turning it into something valuable.

That’s where a data engineer steps in.

Here’s why mastering data engineering skills can outshine a traditional data science degree in today’s job market:

1. Demand for Data Engineers is Soaring

→ Companies need robust data pipelines and clean data before they can even start analysing.

↳ Without data engineers, data scientists can’t do their jobs effectively.

2. HandsOn Problem Solving

→ Data engineering focuses on building and optimising data systems.

↳ You’ll deal with realworld data challenges and provide solutions that have immediate impacts.

3. Versatile Skill Set

→ From coding and database management to understanding cloud services, data engineering equips you with a wide array of skills.

↳ It’s about the whole data ecosystem, not just analysis.

4. Foundation for Advanced Data Exploration

→ Before models can be trained, data must be prepped and organised.

↳ This means your work sets the stage for any advanced data science application.

5. Bridge the Gap Between Theory and Practice

→ While data science often leans towards theoretical aspects, engineering is grounded in practical application.

↳ You’re not just predicting the future; you’re building the road to it.

6. CostEffective Learning

→ You can learn data engineering through online courses, handson projects, and bootcamps.

↳ No need for a hefty degree investment.

7. Innovation at the Core

→ Data engineers are the architects of the digital age, creating systems that allow companies to innovate and grow.

↳ You’re paving the way for future technologies.

So, if you’re weighing options between a degree and diving into data engineering, consider what aligns with the current industry needs.

The world needs more data engineers.

Are you ready to be one?

Engage with me in the comments if you’re considering a career shift or just starting out in data engineering.

What’s your next step?

Want to lead in data? Stop chasing AI buzzwords and start mastering the art of data storytelling.

In the world of data, AI is often the star of the show.

But behind every algorithm is a story waiting to be told.

When I first entered the data realm, like many, I was dazzled by the allure of machine learning and AI.

I thought mastering these would be my ticket to success.

Turns out, I was only half right.

Here's why storytelling matters more than ever:
Stories Resonate
 ↳ Numbers are powerful, but stories connect.
 ↳ They make data relatable.

Simplicity Triumphs
 ↳ Complex algorithms can be intimidating.
 ↳ Simplifying them into a narrative makes your insights accessible.

Actionable Insights
 ↳ Stories guide decisions.
 ↳ They show the impact of data beyond the numbers.

Bridges the Gap
 ↳ Not everyone speaks the language of data.
 ↳ Stories translate insights across diverse audiences.

During my journey, I realised:

Data storytelling isn't just a skill.

It's an art form that demands creativity and empathy.

When I designed and developed a common data platform, it wasn't just about integration.

It was about creating a seamless narrative of connectivity and efficiency.

At NATS, the AI system for air traffic wasn't just a project.

It was a story of innovation and safety.

As I consulted for industry giants, I learned the power of weaving data into a narrative that drove action and transformation.

So, if you're aiming to lead in data:

→ Start with the story.

→ Make your data speak.

→ Inspire those around you.

Don't just be a data professional.

Be a storyteller.

How do you incorporate storytelling into your data projects?

Forget coding boot-camps; real data engineering skills are forged in the chaos of messy data.

When I first dipped my toes into the world of data, I was overwhelmed.

Not by the complexity of coding languages, but by the sheer disorder of raw data.

Here's why dealing with messy data is crucial for any aspiring data engineer:

1. Reality Check: Messy data is the norm, not the exception.

In any business, data doesn't arrive neatly packaged and ready for analysis.

It's scattered, inconsistent, and often incomplete.

Handling this makes you a true problem solver.

2. Creative problem-solving: Cleaning data is more art than science.

You need creativity to find patterns and connections where none seem to exist.

It's like solving a jigsaw puzzle without a picture on the box.

3. Skill Development: Dealing with chaos strengthens your technical skills.

It forces you to master tools and techniques that are often glossed over in bootcamps.

From regex to data wrangling, the experience is invaluable.

4. Adaptability: The ability to adapt to any dataset is a hallmark of a seasoned data engineer.

Real-world data is unpredictable.

The confidence to handle it comes only through practice.

5. Understanding the Business: Messy data often holds the most valuable insights.

It's where you'll find trends and anomalies that can lead to business breakthroughs.

Understanding how data aligns with business goals is key to delivering actionable insights.

For those starting, here's my advice:

→ Embrace the mess.

↳ Dive headfirst into projects that require you to sift through disordered data.

→ Build your toolkit.

↳ Learn the tools that help you clean, transform, and analyse data effectively.

→ Never stop exploring.

↳ Data evolves, and so should your skills.

Remember, coding bootcamps might teach you the syntax, but real expertise is honed in the trenches of messy data.

Ready to roll up your sleeves?

The next time you're faced with a chaotic dataset, see it as an opportunity to sharpen your skills and demonstrate your value.

What has been your experience with messy data?

Share your thoughts or tag someone who's navigating the same path!

I was once told that learning Python was the key to unlocking a career in data.

But here's the truth: It's not just about the language.

It's about developing an analytical mindset.

When I transitioned from being a data analyst to a data engineer, I realised the importance of how I approached problems, not just the tools I used.

Here's why:
Understanding Context: Before diving into coding, understanding the problem is crucial.

What are you trying to solve?

What data do you need?

The ability to ask the right questions is far more valuable than knowing syntax.
Critical Thinking Over Code: Coding is a means to an end.

The real skill lies in analysing data and drawing meaningful conclusions.

Can you see patterns others miss?

Can you make sense of the chaos?
Data Literacy: It's not about how many programming languages you know.

It's about understanding data itself.

How do you clean it?

How do you ensure it's reliable?
Communication: Data insights are useless if they can't be communicated.

Can you translate complex data into simple narratives?

Can you make stakeholders understand the impact?
Adaptability: The world of data is ever-evolving.

New tools, new methods, new challenges.

Being open to learning and adapting is key.

So, if you're thinking of diving into data, remember this:

→ It's not just about Python or any other tool.

↳ It's about fostering an analytical mindset.

And that mindset is what will truly set you apart in the world of data.

What do you think?

Are you ready to start thinking differently?

The shocking truth: Your data pipeline is only as strong as the weakest intern who maintains it.

Sound alarming?

It should.

In the world of data engineering, where precision and reliability are nonnegotiable, the integrity of your pipeline rests heavily on every individual involved.

Yet, we often overlook the importance of nurturing and empowering those at the beginning of their journey—the interns.

Here's how to strengthen your pipeline by investing in your interns:
Comprehensive Onboarding

→ Ensure that interns receive thorough training on your systems.

↳ This isn't just about technical skills.

It's about understanding your organisation's data philosophy and goals.
Mentorship

→ Pair interns with experienced data engineers.

↳ Real-world guidance and feedback are invaluable.
Safe Learning Environment

→ Create a culture where mistakes are seen as learning opportunities, not failures.

↳ Encourage questions and exploration.
Clear Responsibility

→ Define their roles and responsibilities clearly.

↳ Clarity prevents errors and builds confidence.
Regular Check-ins

→ Schedule regular meetings to discuss progress and challenges.

↳ Continuous feedback loops help in identifying issues early.
Celebrate Achievements

→ Recognise and celebrate their contributions.

↳ Acknowledgement boosts morale and motivation.

By empowering your interns, you're not just securing your data pipeline.

You're investing in the future leaders of your data teams.

Remember, a robust pipeline isn't built on code alone.

It's built on a foundation of well-trained, engaged, and motivated individuals.

So, next time you review your data processes, ask yourself:

Have you given your interns the tools they need to succeed?

Your pipeline depends on it.

What strategies have you implemented to support your interns?

Share your thoughts!

taplio

The Battle

Dobromir S.

Dr Emmanuel Ogungbemi

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taplio

The Battle

Dobromir S.

Dr Emmanuel Ogungbemi

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Dr Emmanuel Ogungbemi's LinkedIn Performance

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