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Production ML, Not Notebook Demos

RAG systems, AI agents, and predictive models that ship to real users.

What I build

RAG Pipelines

Embeddings, retrieval with confidence scoring, citation surfacing. pgvector, LangChain, FastAPI. Served as REST API or embedded widget.

AI Agents with Real Tool Access

Multi-agent orchestration via CrewAI. MCP servers connecting agents to SQL databases, internal APIs, and documentation repos, not toy chains.

Predictive ML for Tabular Data

Classification, regression, and anomaly detection using CatBoost, XGBoost, LightGBM, and ensembles. Benchmarked against your baseline, not just cross-validated in a notebook.

OCR & Document Extraction

Structured JSON extraction from scanned PDFs. Azure Document Intelligence or in-house GLM-OCR depending on cost/accuracy tradeoffs.

Recent work

Stack

ML & AI

  • - PyTorch
  • - scikit-learn
  • - CatBoost
  • - XGBoost
  • - LightGBM
  • - pandas
  • - NumPy

GenAI & NLP

  • - LangChain
  • - CrewAI
  • - MCP
  • - FinBERT
  • - Transformers
  • - OpenAI API
  • - Gemini

Backend

  • - Python
  • - FastAPI
  • - Node.js
  • - TypeScript
  • - Golang

Cloud & DevOps

  • - Azure
  • - AWS
  • - Docker
  • - GitHub Actions
  • - Linux

Databases

  • - PostgreSQL
  • - pgvector
  • - SQL Server
  • - Redis
  • - MongoDB

How I work

I work async-first and document as I go. Paid 2-hour scoping call before any engagement over 20 hours so we both know the scope is real. Fixed-price for well-defined deliverables, hourly for open-ended exploration. Based in Cork, Ireland, EU business hours with meaningful overlap for US east-coast mornings.

Work together?