ML Engineer, RAG Systems, AI Agents, and Predictive Models
I build production ML systems, RAG pipelines with pgvector, AI agents wired to real databases via MCP, gradient-boosting predictive models, and OCR extraction pipelines. I previously shipped ML features at CompuCal (Cork, Ireland) and am returning to MTU Cork to complete a Level 8 BSc (Hons) in Software Engineering.
RAG Pipelines
Embeddings, retrieval with confidence scoring, and citation surfacing. pgvector, LangChain, FastAPI, served as a REST API or embedded widget.
AI Agents with Tool Access
Multi-agent orchestration via CrewAI. MCP servers connecting agents to SQL databases, internal APIs, and documentation repos, not toy prompt chains.
Predictive ML
Classification, regression, and anomaly detection with CatBoost, XGBoost, LightGBM, and ensembles. Benchmarked against a real 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 and accuracy tradeoffs.
My Journey
I started with Python scripts in early 2021, automating crypto trades was the excuse to learn the language. Five years on, the work has settled: production ML for small teams. RAG systems, AI agents with real tool access, and predictive models for tabular and document-heavy problems. After shipping ML features at CompuCal, I'm returning to MTU Cork to complete a Level 8 BSc (Hons) in Software Engineering.
Education
Returning to MTU Cork to complete a Level 8 BSc (Hons) in Software Engineering. QQI Level 6 Advanced Software Development with distinction prior.
Previous Experience
Previously shipped ML features at CompuCal (Cork, Ireland), RAG, OCR, predictive maintenance, and MCP-backed agents for a regulated calibration-management platform.
Focus
Production ML for small teams: retrieval systems, AI agents, and predictive modelling for tabular and document-heavy problems.