Bartosz Mikulski - AI and MLOps related experience

Projects

  • Fixing hallucinations in a no-code pipeline

    Fixed hallucinations and parsing errors in the output of data extraction AI for a no-code pipeline.

    Company: Can't disclose AI Technologies: BAML
  • Laoshu.ai

    Laoshu is an open-source tool that helps you verify the citations used by AI.

    Company: Open-source project AI Technologies: BAML
  • Retrieval Augmented Generation for Customer Support

    Implemented the semantic search solution based on a vector database. Performed data analysis, including clustering and topic modeling, to find types of past support requests. Used GenAI to draft a suggested solution to new customer support requests.

    Company: Can't disclose AI Technologies: LangChain, Chroma
  • AI-based reporting solution

    Worked on an AI-based solution for analyzing online reviews and comparing the performance of different branches of the same company.

    Company: Can't disclose AI Technologies: LangChain Read case study
  • Machine Learning Platform

    Worked on Qwak Machine Learning platform and its Python SDK.

    Company: Qwak AI Technologies: BentoML
  • MLOps pipeline

    Built a production-grade MLOps pipeline with automated testing, monitoring, and zero-downtime model releases for high-stakes ML systems.

    Company: Riskmethods AI Technologies: AWS Sagemaker, PyTorch, MLFlow, BentoML
  • Expert system for data normalization

    Built a smart data pipeline that automatically detects and merges duplicate business partner records, learning from human feedback to improve accuracy over time.

    Company: Riskmethods
  • Automated bidding software

    Built machine learning models that accurately predicted revenue-per-session to drive high-impact, automated ad bidding and maximize ROI.

    Company: Pub Ocean AI Technologies: XGBoost, Tensorflow

Testimonials

  • Martyna Urbanek-Trzeciak (Product Manager - Data Engineering)

    I worked with Bartosz while he was a member of Data Engineering team at Fandom. He is very professional and open to share his knowledge with his teammates and beyond. His approach was always very data-driven and he has great knowledge in Data Engineering area what made him very valuable partner in discussions.

  • Mariusz Kuriata (Senior Manager of Engineering - Head of Ops)

    It was my pleasure to work with Bartosz. Bartosz is a dedicated and experienced Data Engineer who showed a range of skills and readiness to help. I appreciated that I could count on Bartosz to lead sophisticated technical projects. Highly recommend!

  • Workshop participant

    I'm extremely impressed with Bartosz's expertise and experience. We covered all assignments, addressing various details, scenarios, and potential errors. Every question we asked was answered thoroughly. The workshop format of the sessions and small group activities were particularly enjoyable. We had opportunities to apply our new knowledge practically. The trainer remained accessible whenever questions arose. If any uncertainties emerged, the facilitator explained everything with patience."

  • Workshop participant

    One of the most content-rich lessons I've experienced.

Workshops I teach

  • Building AI-Powered Applications with LangChain

    I teach how to craft effective prompts, and use LangChain to extend model capabilities (connecting to the internet, vector databases, and external REST endpoints). Together we build a memory-aware chatbot, implement retrieval-augmented search over document collections, design multi-step reasoning chains and autonomous agents, and wrap everything with LangSmith for monitoring and logging. By the end, attendees leave with deployable code, a solid understanding of the modern AI stack, and the confidence to embed intelligent functionality into their own products.

    Read more
  • Business Process Automation with No-Code and AI

    Participants learn to map processes visually in n8n, add branching logic and loops, and connect to any SaaS or in-house API. They learn to include AI in the workflows for text summarization, classification, vector search across files and databases, and build tool-using agents that fetch live data or trigger follow-up actions. By the end, they can develop a customer-support chatbot, automate document creation, send contextual Slack or email alerts, and monitor everything from a single, no-code dashboard.

    Read more
  • Retrieval-Augmented Generation (RAG): Building AI Search Systems

    Participants learn how to parse and index documents into vector stores, craft advanced retrieval strategies (semantic search, query expansion, keyword/metadata filters, parent-document and sub-query retrieval), and combine results with reranking for higher relevance. They practice scoring both retrieval and generation with robust metrics, integrate text-to-SQL so LLMs can mine relational databases, and apply guardrails for automatic answer verification to keep responses factual.

    Read more
  • Fine-Tuning Language Models

    Participants learn the whole fine-tuning pipeline: choosing the right approach (full fine‑tune, LoRA, QLoRA), preparing clean training data, and running experiments with Axolotl or HuggingFace Transformers. We cover hyper-parameter choices, cost controls, and automated ways to score the output using larger reference models. By the end, they can ship a custom model, prove its quality with solid metrics, and expose it through a reliable API.

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  • Prompt Engineering

    Participants learn why models hallucinate, what they can and can't do, and how to craft prompts that steer them: defining clear context, structuring questions, setting explicit output requirements, and iterating systematically. We practice advanced techniques like few-shot examples, chain-of-thought and tree-of-thought prompting, and robust system messages.

    Read more

Conference Talks and Podcasts about AI and MLOps

  • MLOps for the rest of us at Infoshare 2022 (Gdańsk, Poland)

    Shared how my team built a lean MLOps pipeline that let us deploy new machine learning models to production without overengineering or big budgets. I covered real-world lessons from supply chain risk management, including handling word embedding failures, moving to Sagemaker, testing preprocessing, and managing canary releases.

    Read more
  • Data Intensive AI at DataTalks Club hosted by Alexey Grigorev

    We discuss practical strategies for testing data workflow. I explain how data engineering underpins effective AI, from preparing training data to deploying models, and highlight real-world use cases where AI quietly powers better products behind the scenes. We dive into prompt engineering, showing how in-context examples and evaluation datasets drive reliable outputs, and touch on emerging topics like prompt compression and caching.

    Read more

Publications

Other Conference Talks

Other Podcasts

Meetups

How to contact me?

You can find me on social media (links below) or send me an email: blog (here is the "at" symbol) mikulskibartosz.name

Social Media

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