Paul Iusztin

🤖 Senior Machine Learning Engineer • MLOps • Contractor ~ Helping startups engineer production-ready ML/AI systems.

I'm the author of the bestseller LLM Engineer's Handbook, lead instructor of the Agentic AI Engineering course, founding AI Engineer of a San Francisco start-up, and obsessed with making knowledge accessible through AI.

With over 10 years of experience in AI and software and 20 apps shipped, I teach AI Engineering as I wanted to at the beginning of my career. End-to-end. From idea to production. From data collection to deploying, monitoring and evaluation. With a focus on AI principles, software patterns and infrastructure systems that will thrive in a future dominated by AI coding tools.

My ultimate goal is to help other engineers escape PoC purgatory and 10x their AI Engineering skills.

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If you want to become an AI Engineer pro, join Decoding AI for exclusive content on designing, building, and shipping AI software that works: 

🗞️ https://www.decodingai.com

See more:

🌐 Homepage: https://www.pauliusztin.ai
📚 LLM Engineer's Handbook: https://www.amazon.com/LLM-Engineers-Handbook-engineering-production/dp/1836200072/
🤖 Agentic AI Engineering Course: https://academy.towardsai.net/courses/agent-engineering?ref=b3ab31

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✉️ For collabs, contact me at: p.b.iusztin@gmail.com

Senior Machine Learning Engineer

🟡 Scalable ML batch infrastructure for continuous training & inference

The client, a generative AI leader, struggled with manual training and calling their ML models. As a tedious and unscalable process, they needed to automate their ML processes completely and integrate them with their current web platform.

➞ 𝐃𝐞𝐬𝐢𝐠𝐧𝐞𝐝 and 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐞𝐝 a modular 𝐏𝐲𝐭𝐡𝐨𝐧 𝐩𝐚𝐜𝐤𝐚𝐠𝐞 that composes the ML model required steps into 3 main batch pipelines that can easily be automated: feature, training, and inference, 𝐞𝐥𝐢𝐦𝐢𝐧𝐚𝐭𝐢𝐧𝐠 a 𝐭𝐞𝐝𝐢𝐨𝐮𝐬 𝟐-𝐡𝐨𝐮𝐫 𝐦𝐚𝐧𝐮𝐚𝐥 𝐩𝐫𝐨𝐜𝐞𝐬𝐬/𝐯𝐢𝐝𝐞𝐨.
➞ 𝐃𝐞𝐩𝐥𝐨𝐲𝐞𝐝 a scalable & cost-effective 𝐛𝐚𝐭𝐜𝐡 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 and 𝐂𝐈/𝐂𝐃 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 on top of AWS ECS & SQS that scales in and out based on the number of jobs, 𝐬𝐚𝐯𝐢𝐧𝐠 𝐭𝐡𝐨𝐮𝐬𝐚𝐧𝐝𝐬 𝐨𝐟 𝐝𝐨𝐥𝐥𝐚𝐫𝐬/𝐦𝐨𝐧𝐭𝐡.
➞ 𝐒𝐚𝐯𝐞𝐝 𝐦𝐨𝐧𝐭𝐡𝐬 𝐨𝐟 𝐟𝐮𝐭𝐮𝐫𝐞 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 by using good software practices to make the Python module & batch cloud infrastructure flexible and composable.
➞ 𝐁𝐮𝐢𝐥𝐭 a 𝐯𝐨𝐢𝐜𝐞 𝐬𝐲𝐧𝐭𝐡𝐞𝐬𝐢𝐬 𝐏𝐨𝐂 in ~1 week required to 𝐜𝐥𝐨𝐬𝐞 the 𝐝𝐞𝐚𝐥 with one of the 𝐜𝐮𝐫𝐫𝐞𝐧𝐭 𝐜𝐥𝐢𝐞𝐧𝐭𝐬.

⚫️ Big data ingestion & archiving pipeline

The client wanted to expand its platform features by securely and safely ingesting, preprocessing, and storing TB of data into their platform.

➞ 𝐂𝐨𝐧𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 to the 𝐝𝐞𝐬𝐢𝐠𝐧 and 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 of a 𝐝𝐚𝐭𝐚 𝐢𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 that supports 𝐢𝐧𝐠𝐞𝐬𝐭𝐢𝐧𝐠 >𝟓 𝐓𝐁 of data in 𝐨𝐧𝐞 𝐠𝐨 in a reliable, fast, and secure way.
➞ 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐞𝐝 a scalable 𝐝𝐚𝐭𝐚 𝐩𝐫𝐞𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 that supports multiple media types (images, videos, audio, 3D objects) and creates small preview files that are cheap to transfer, store and visualize. The 𝐨𝐫𝐢𝐠𝐢𝐧𝐚𝐥 𝐟𝐢𝐥𝐞𝐬 are 𝐚𝐫𝐜𝐡𝐢𝐯𝐞𝐝 on an affordable storage solution 𝐫𝐞𝐝𝐮𝐜𝐢𝐧𝐠 𝐜𝐨𝐬𝐭𝐬 𝐛𝐲 𝐮𝐩 𝐭𝐨 𝟖𝟏%.

I built a system that turns a single idea into a publish-ready technical article.

Here's a mini masterclass on how (so you can copy):

The system consists of 2 parts:
 
• Nova (research agent): exploratory, steerable
• Brown (writing workflow): deterministic, auditable

𝟭) 𝗡𝗼𝘃𝗮: 𝗦𝘁𝗲𝗲𝗿𝗮𝗯𝗹𝗲 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗔𝗴𝗲𝗻𝘁 (𝗲𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻)

Goal: ingest context from messy sources, fill gaps, and produce a clean research artifact.

Inputs:
 
• Structured article brief (topic, audience, structure, constraints, candidate sources)

How it runs:
 
• MCP-first design so tools are portable across harness clients
• A prompt exposed as procedural memory to plan and glue all stages
• Tooling focused on ingesting context, not “thinking in tokens”

Core stages:
 
• Pull context from URLs, local files, GitHub repos, YouTube transcripts
• Run multi-round research loops (Perplexity for cited web search)
• Filter for trust and relevance
• Deep scrape the top sources
• Consolidate into a single handoff artifact: research.md

Output:
 
• A curated research document with organized notes and citations, ready for drafting

𝟮) 𝗕𝗿𝗼𝘄𝗻: 𝗥𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗪𝗿𝗶𝘁𝗶𝗻𝗴 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 (𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻)

Goal: turn research into a consistent article, with quality gates and checkpoints.

Why a workflow, not an agent?

Because writing quality demands:
 
• Repeatability
• Checkpoints
• Clear stage boundaries
• Traceability when something is off

How it runs:
 
• LangGraph for stateful orchestration, resumability, and auditability
• Leveraging tools as deterministic workers instead of exposing them to agents

Core stages:
 
• Load context: guideline + research.md + writing profiles
• Generate required assets in parallel (diagrams, images, tables when needed)
• Draft the article
• Run review and rewrite loops (reflection scores, style judge, revision triggers)
• Support human edits without breaking the system’s rules

Outputs:
 
• Final article
• Generated assets + metadata
• Reflection scores and traces (so you debug quality instead of guessing)

Here’s the gist…

If you promise “production-grade writing”, you need exploration for research and durability for execution.

This design choice is the difference between a cool demo and a system you can ship.

Want to learn how to build it out yourself?

We break this down step-by-step in the Agentic AI Engineering course (built with Towards AI, Inc.):

Enroll here: https://lnkd.in/dtxZuZpb

P.S. We’ve opened up another 100 seats for $499. The last batch sold out in under 14 days → get in early to lock in our best offer.

Senior AI Engineer • Founder @ Decoding AI • Author @ LLM Engineer’s Handbook ~ I ship AI products and teach you about the process.

Thoughtworks

Staff Generative AI Engineer

Enterprise Search: Contributed to a major global market research leader in the U.S. by developing an Enterprise Search application enhanced with Generative AI. As part of a big data and development team, I'm responsible for the integration of generative AI within the application.

Responsibilities:

-- Generate and curate synthetic datasets with generative AI to fine-tune Large Language Models (LLM).
-- Fine-tune Gemini flash and open-source SLM (Small Language Models) for precise multi classification tailored to the enterprise search needs.
-- Develop the RAG component of the application, capable of handling complex structured files at large scale.

Generative AI Engineer | Senior Consultant

Remote to US & LATAM
>> Role :
---- Responsible  for providing expert consultation in Generative AI initiatives for multiple clients of the company and develop projects to showcase to clients how to use Generative AI effectively in their business.

>> Projects:
--Proxxima AI for Marketing Strategy  > Hyper-Personalization based on Product Review
V1 : Used ChatGPT API + Langchain to develop an Application that :
> Generates a summary of the reviews of selected products
> Generates a Persona using selected reviews of a customer
> Creates personalized engagement messages based on customer-selected reviews

V2 : Migrated application to integrate with Vertex AI (PaLM2 - Bison ) + Langchain
> Created and curated the training dataset of English product reviews based
on a sample of the original Portuguese dataset.
> Translated all the application to English and refactored scripts for the new data integration
> Modularized Model integration for easier flexibility, allowing for changes in the future if necessary. Added Error handling and logging to the scripts.
> Generate summary, personas and hyper-personalization engagement emails.

>> Other Solutions:
Developed the solution for the Pinecone Vector Database migration to the client's organization, by upserting over 500k vectors of dimension 1536 by interacting with the company's and the client's Pinecone APIs.

| Stack : Langchain , LLM , ChatGPT API , Vertex AI , GCP , Bison-PaLM2,  LLM Function Calling , Multi-tool Agents, Customized tools , Multimodal applications , Prompt Engineering , Poetry , Python , Streamlit , Docker , RAG - Vector Database, APIs , Websites.

Data Scientist | Senior Consultant | Generative AI

Stealth Mode AI Startup

Led Generative AI projects for startups and consultancies, project-based, from gathering client requirements to designing and implementing solutions. Built MVPs and PoCs to turn ideas into working prototypes. Mentored data and software teams on GenAI, guiding practical applications and creating learning paths to empower them in developing AI solutions.
_______________
|| Key Projects || part 1

- SQL Agent: Designed and developed an MVP of a ReAct SQL agent using a multi-agent architecture for Natural Language Query (NLQ), allowing non-technical users to translate their questions into SQL queries tailored to the company's database structure. The agent automatically discovers relevant tables and schemas based on user queries, making data access more intuitive.
>> Stack: LangGraph, Langchain, Langsmith, OpenAI, Python

- LLM Routing: Developed a strategy to optimize system costs and improve reliability by dynamically selecting the best AI model for each task of a customer service multiagent system.
>> Stack: OpenRouter, Langchain, LangGraph, OpenAI (GPT-4o-mini, GPT-4o), Anthropic (Claude Sonnet), Mistral AI (7b)

- Advanced RAG System: Built a PoC for a RAG system using advanced techniques (C-RAG, Self-RAG, Adaptive-RAG). The system retrieves information from a vector database or performs a Google search based on user queries, providing precise and context-aware responses, and uses 2 layers of self-rag to check for hallucinations and relevance, ensuring the response directly answers the user's question.
>> Stack: LangGraph, Langchain, Langsmith, OpenAI, Python

- Multimodal Embedding: Developed a PoC for a RAG system with support for multimodal embeddings, enabling it to understand and process PDF tables, images, and hierarchical text structures, to interpret complex documents and answer user queries accurately.
>> Stack: JinaAI (multimodal embedding, CLIP model), Unstructured API (chunking strategy), Qdrant (VecDB), Langchain, Langsmith, OpenAI, Python

Generative AI Engineer | Remote to US, UK, Europe & LATAM

_______________
|| Key Projects || part 2

- Multi agents System for full Lead Scoring and classification : 
Developed the MVP of a multi-agent system chatbot with RAG. This chatbot interacts with users, introduces the company, and captures basic user and company information from the chat. Based on the basic informations, it then performs Google searches to gather detailed company data required for the complex business rules to classify leads, calculate lead scores, and categorize potential leads. The chatbot also writes personalized messages based on recent milestones of the user's company found online for better engagement and tailored outreach. Then, it showcases similar use cases relevant to the user's expertise request and industry that the consultancy has successfully delivered, providing evidence of past successes and relevance to the user's needs.
>> Stack: CrewAI, OpenAI, Python, FastAPI

- Summary Generation and Insight Extractor of Financial Statements in Excel : 
Developed and implemented a Generative AI solution to automate the summarization of complex financial statements in Excel for one of Germany’s top banks. The solution provided in-depth insights, including detailed analyses of subtables, concise summaries of individual components, and a comprehensive overview of the entire document. This innovation transformed complex financial data into clear, actionable insights, significantly enhancing data comprehension and supporting better decision-making. The solution enabled faster processing of intricate datasets, empowering users to focus on strategic analysis rather than manual data aggregation.
>> Stack: Langchain, OpenAI, Python

Generative AI Engineer

Customertimes

• Architected scalable solutions using AWS : Designed end-to-end data pipelines and data intensive systems to efficiently extract, transform, and load large-scale datasets. Prepared technical and non-technical presentations to effectively communicate project architecture and benefits to final clients.
>> S3 , Glue , Lambda , SQS , SNS , RDS - Postgres , Redshift , Python , Spark , SQL , Great Expectations , Data lake , Data warehouse 

•  Orchestrated and guided the team in creating effective strategies to build a reliable and scalable system, using data engineering and software engineering best practices:  
Automated tests (unit , integration tests) as part of the CI/CD pipeline, IaC, Data modeling to optimize system overall performance, Version control , Techical documentation (architecture diagrams, data models, system dependencies), Error handling , Logging.

• Implemented Data Governance practices: Designed and implemented data governance frameworks, including data lineage tracking, data quality validation, and data privacy controls. Included testing strategies within the data governance framework to verify data quality and integrity 

• Designed code patterns for maintainability and scalability: Developed robust code design patterns aligned with industry best practices, emphasizing code quality, reusability, and scalability. Implemented data quality validation techniques to ensure the accuracy and reliability of data.

• Be the Technical leader of a team of senior data engineers, fostering a collaborative environment and empowering them to deliver high-quality work. Coordinated data governance initiatives, including data lineage, data quality validation and data modeling.

• Collaborated with stakeholders and cross-functional teams: Worked closely with stakeholders, including business analysts and solution architects, to understand requirements and translate them into scalable data solutions , to ensure seamless integration and alignment across the project.

Data Architect | Lead Data Engineer

ioasys

• Propose and deliver End-to-End Data Solutions for CRM team in Brazil and BI team in the US - Machine Learning, dashboards, reports, automation pipelines, analytics - based on business needs and requirements from stakeholders.

• Lead teams of data scientists, data engineers, and data analysts in designing and implementing Data Solutions, from research and architecture to development and deployment, serving as the technical reference for the team - Data Solutions Architecture, Agile SCRUM .

• Develop and maintain data pipelines, from data ingestion in all layers of the data lake to serving it to Data Solutions - AWS Glue, Databricks, Tableau, PowerBI, Snowflake, Docker, AWS, Python, Pyspark, SQL, APIs.

• Drive changes to improve the efficiency and organization of the entire data team - Data Governance and Data Lineage processes.

Main Achievements:

- Proposed and led a team of 5 data scientists in the implementation of Customer Segmentation Analytics using RFM, LTV, and Product Analytics for all US customers. The results were used by the Marketing and E-commerce teams to build the strategy for the year 2023, then adopted by the brazilian CRM team

- Troubleshooting of  data pipelines related to data modeling and performance - Time zone issues, data modeling performance optimization ;

- Proposed a new data modeling approach for strategic dimensions, focusing on maintaining historical data change to enable more accurate trend analysis -  SCD type 2 

- Proposed and implemented a new data modeling approach for dashboard data, addressing a key stakeholder pain point related to dashboard performance.

- Implemented a manual Data Lineage for CRM resulting in a significant decrease in onboarding time for new data team members, from 2 weeks to 2 hours.

- Proposed data governance and lineage strategies, advocating for increased visibility and insights for the data team -  Automated solution to replace manual process. Responsible for the PoC of data lineage products .

Lead Data Scientist and Data Engineer

Somos

Somos is a legal tech Startup that aims to fight for the dignity of people around the globe that suffer from injustices, helping law firms manage and analyze their Data of collective claims.

I was responsible for creating a solution so that our deliverable would be more elegant, scalable and highly available than an Excel Sheet. Using the Business Acumen  from the past months of work, I suggested a solution that could allow our lawyer clients to simulate negotiation scenarios with the counterpart, get the Data Analysis of those scenarios in real-time, filter the data to focus on specific features and export/share their results with their teams. 
My first key role was to perform the POC of the core tools to build this product. After a successful presentation to the CTO , my second key role was to put together and train a cross-functional team to build and productize the full solution.

• Stack: Streamlit, GIT (Bitbucket), Docker, Docker Compose, Virtual Environments, Plotly, Python, AWS ECR , ECS Fargate , RDS , Lambda ,  SQS , S3 etc.

My third key role is to research the market best practices on Data Products Development and Architecture, in order to capacitate our team to build competitive products.

• Trainings : 
| Virtual Environment : What and Why
| Code Version Control : Git
| Containers : Docker
| Containers Orchestration : Docker Compose

Squad Lead | Data Product

I started at Somos as a Senior Data Scientist , cleaning and organizing data ( ETL ) , creating insightful data analysis with Python and being responsible for the Data Strategy of the Questionnaires of the collective claims, working side by side with our international clients at PGMBM in London - UK .

- Developed and implemented a data strategy using NLP , FuzzyWuzzy logic and analytics to identify and analyze clusters of victims of Brumadinho's Dam Collapse in order to compute how much each victim is entitled to according to business rules.

- Developed and led the implementation by the Data Engineering team through the Data Strategy to analyze the big data scenario at the Uber Litigation, dealing with Uber's data of all events of each trip of 1,200 drivers for the last 5 years. 

• Stack : AWS Sagemaker, AWS Athena , AWS S3, Python , SQL , Machine Learning , Natural Language Processing (NLP), Pandas, Numpy, Scikit-Learn, Matplotlib, Seaborn , Jupyter Notebook , git (Bitbucket)

Also, I'm responsible for being the technical reference by my teammates regarding Data Analysis and Data Science, as well as Data Strategy decisions. My third key role is to elaborate and create internal trainings to my team to explain our new implemented working method. 

• Trainings :  
| Agile Methodologies - SCRUM vs. KANBAN 
| KPIs, OKRs and Sprints

I also conduct technical interviews of Senior Data Scientists and Data Analysts candidates, in both Portuguese and English .

Senior Data Scientist

iFood

iFood is a Brazilian on-demand food delivery platform Startup that became a unicorn in November 2018.
My main responsibility is to act as the bridge between my business analyst team and iFood's Data team, as I'm the only one with a Data Scientist background.

》Data Analysis and ETL
》Machine Learning solutions
》Process Mining
》Identify Business Improvement opportunities with Data Science
》Stack : Python, PySpark, Databricks, SQL, Tableau, DriverlessAI (H2O autoML), Celonis EMS.
》Other Roles :
    • Guild Data with Stats : Being part of the organization, I was responsible for the development of the data driven culture by showing the fundamentals of statistics and machine learning to all collaborators, organizing the meetings, topics, recommending materials and articles.

   • Founder of the Debates group: Bi-weekly meetings to discuss articles about Business, Growth, Data driven culture and Professional development in the current market, promoting integration among areas and seeking innovative solutions for the Business. Main Sources: Towards AI, Medium, MIT Technology Review, Harvard Business Review etc.

Senior Data Analyst

MC1 - Win The Market

》Data Analysis with Python
》Machine Learning models
》ETL with SQL

Data Scientist

Upwork

Data Wrangling (cleansing, enrichment, transformation), Data Analysis, Data Visualization , BI Reports. 
| Stack: Python, SQL, pandas, matplotlib, seaborn, jupyter notebook

Data Scientist | Data Engineer

Career Specialization

- Main Achievements:

• Machine Learning - Stanford University 
• Machine Learning Professional Certificate - IBM
• Applied AI Professional Certificate - IBM
• Data Science Professional Certificate - IBM
• Artificial Intelligence Formation - Data Science Academy
• Industry 4.0 Engineering Specialization - UNOPAR
• Python for Data Analysis - Data Science Academy
• Artificial Intelligence Fundamentals - Data Science Academy
• Python Essencials - Python Institute + Cisco

|| Professional Badges || https://www.youracclaim.com/users/bruna-lemberck
|| GitHub ||  https://github.com/lemberck

At the beginning of 2020 I decided to focus on Innovation. In order to research what is Innovation nowadays, I took a Specialization in Industry 4.0 Engineering, where I found out a great passion for Big Data and Cognitive Computing. 
From that time until present days, I've been studying and practicing on projects to work on my Machine Learning, Artificial Intelligence and Data Science skills, and decided to start a career in these areas in 2021.

I've worked on over 30 projects, and some of them can be found here in my Linkedin and also here : https://github.com/lemberck

Machine Learning, Artificial Intelligence and Data Science

EMATER-RIO

EMATER - Technical Assistance and Agriculture Extension Company. Brazilian state-owned company  responsible for Assistance and support at technical projects and resolutions for the ariculture and countryside in the State of Rio de Janeiro/BRA.

• Developed projects for clean and sustainable technologies with application and operation of road projects, rural sanitation, reforestation and protection of watersheds in the municipalities of the State of Rio de Janeiro;
• Evaluated operating conditions and adjusted settings to maximize performance and equipment longevity;
• PLC ladder programs troubleshooting using RSLogix;
• Project auditing of Automation Projects contracted by the autarchy.

Equipment Engineer

I'm a Data Professional ( 7+ years ) with a background of Industrial Automation Engineering , passionate about and committed to Continuous Learning. 

>> Currently with great interest in designing and implementing products powered by GenAI and Multi Agents systems

I'm dedicated to seeking business value through Data - Data transformation pipelines, Data Architecture, Data Analysis, Machine Learning models - with focus on problemsolving from simple to complex challenges by building end-to-end Data Solutions. I value the continuous learning to keep up with the market best practices and technologies that could potentially bring a competitive advantage to Business. 

English Level : C2 (written , spoken)
Current Location: Rio de Janeiro, Brazil

|| Experience: 
> GenAI powered Systems : Design Multiagents systems for workflow automation, Advanced RAG (Retrieve Augmented Generation) with multiagents architecture, AI Agents with tools tailored for the requirements, Flow Engineering, Chatbots, OpenAI , Anthropic (Claude), VertexAI (Gemini), Azure OpenAI, Langchain, Langgraph, CrewAI, Prompt Engineering, LLMOps.

> Data Solutions Architect - AWS 
> Data Governance
> Data analysis , Data Modeling
> Machine Learning , Data Science , Data Engineering
> Data Lake , Data Warehouse , Data Mart - PostgreSQL, Redshift, Snowflake
> ETL , ELT
> Python , Pyspark , SQL
> Data Transformation Pipelines
> FastAPI
> Pydantic
> Containers - Docker
> Azure Databricks 
> Leadership , team building
> SCRUM

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