Frame
Decision, users, sources, risks and success criteria.
EXPERTISE
Engineering useful, observable generative AI systems.
Technical capabilities translated into what they enable for a product and its users.
Back to the corresponding chapter ↑I connect models, data, interfaces and quality control so AI produces a usable result—not merely a plausible answer.
Generative AI
- LLM applications
- RAG
- AI agents
- Document intelligence
- Evaluation
- LangChain
- Langfuse
Product engineering
- Python
- Angular
- React
- TypeScript
- APIs
- SSE
- MCP
- MongoDB
- Milvus
- PostgreSQL
Delivery and systems
- AWS
- Azure
- CI/CD
- Jenkins
- Datadog
- Git
- Agile / Scrum
- Performance modelling
- Technical writing
METHOD
From the need to an observable system.
Build
Retrieval, agents, document processing, APIs and interfaces.
Observe
Tracing, sources, execution steps and operational signals.
Evaluate
Quality checks, limits, feedback and controlled iteration.
APPLICATIONS
What these capabilities enable.
Financial AI agent
Documents and financial data become a sourced SWOT analysis and a reusable presentation.
Document intelligence
LLMs support the analysis of business documents within the PICXEL project.
RFP assistance
An internal tool helps managers understand opportunities and prepare responses.
Performance research
An analytical model evaluates network-on-chip performance up to 500× faster in the reported research comparison.
FAQ
Frequently asked questions
What type of role are you looking for?
A generative AI engineering role involving LLM applications, RAG, agents, document intelligence and production concerns.
Can you work beyond the model layer?
Yes. The documented projects include Python services, Angular and React interfaces, APIs, databases, document pipelines, cloud and CI/CD tooling.
How do you handle LLM reliability?
By grounding work in explicit sources, exposing execution steps, adding observability and evaluating outputs against defined criteria. The exact evaluation protocol depends on the use case.
I bring a research mindset, end-to-end engineering and a focus on traceable outputs to generative AI products.