I build production-ready software systems across backend development, full-stack applications, AI/ML, LLM integrations, automation, data pipelines, and cloud infrastructure.
I currently work as a Software Engineering Team Lead at ENIT/NITES, where I lead development of production systems used in the healthcare and energy sectors. Alongside industry work, I am an active software systems researcher working across AI, Machine Learning, Cloud Computing, Distributed Systems, and performance engineering.
I am especially useful when a project is more complex than basic CRUD development and requires several parts to work together reliably.
What I can build work on:
- Backend systems and REST APIs
- Full-stack web applications
- Python automation and custom internal tools
- AI-powered applications and LLM integrations
- RAG systems, embeddings, semantic search, and vector databases
- AI agents and automated workflows
- Document AI, OCR, PDF and image processing
- Speech-to-text and multimodal processing pipelines
- Machine Learning pipelines and predictive models
- Forecasting, anomaly detection, and data analysis
- API and third-party service integrations
- Data pipelines and ETL workflows
- PostgreSQL, MySQL, Redis, Elasticsearch, and Kafka
- Java / Spring Boot, Python / FastAPI, Go, and React development
- Docker, Kubernetes, cloud deployment, and infrastructure
- Distributed systems, queues, caching, retries, idempotency, and reliability
- Debugging, performance optimization, and extending existing codebases
My public work includes systems involving transactional messaging, Kafka event pipelines, Kubernetes operators, scalable cloud infrastructure, document intelligence, OCR and speech transcription, ML forecasting, anomaly detection, search and caching architectures, AI-assisted workflows, and complete full-stack platforms.
I am comfortable both building something from scratch and entering an existing codebase, understanding how it works, fixing the problem, adding the requested functionality, testing it, and delivering a clean result.
My approach is outcome-focused: understand the requirement, choose the appropriate technology, build the solution, test it properly, and deliver something that actually works.
If you have a clearly defined technical problem, an idea that needs to become a working product, an existing system that needs fixing, or a workflow that should be automated, provide the requirements, expected outcome, repository details, and any relevant constraints. I can usually determine very quickly whether the project is a strong fit for my experience.