AI Engineer · Generative AI · Backend Specialist
AI Engineer building intelligent, production-ready applications with LLMs, RAG pipelines, AI agents, and intelligent automation. I combine Python, Django/FastAPI, and modern AI engineering to turn foundation models into reliable systems that solve real-world problems.
01 — About
I’m a Generative AI Engineer with a software engineering background, focused on building practical AI applications around large language models. My engineering foundation began with Python and Django, where I developed experience designing backend systems, APIs, database-driven applications, and production-oriented software.
Today, I apply that foundation to Generative AI engineering, working across LLM applications, Retrieval-Augmented Generation (RAG), prompt engineering, AI agents, tool integration, and intelligent workflows.
I approach GenAI as a software engineering discipline: combining models, data, backend services, and application logic to create AI systems that are reliable, useful, and ready for real-world use.
02 — Skills
Grouped by where each piece sits in an AI application — from language to model to interface. Kept to technologies actually used in shipped work.
03 — Projects
A showcase of production-ready applications.
An AI-powered prompt engineering workspace that scores, critiques, and rewrites prompts before they're sent to a model — combining deterministic Python analysis with Gemini-driven semantic evaluation.
An autonomous CI/CD agent that ingests pull requests, executes static code analysis alongside LLM-driven security audits, generates failing test cases, and automatically commits self-healing patch suggestions.
A multi-PDF intelligence platform: hybrid retrieval combining BM25 keyword search with dense vector search in Qdrant, cross-encoder re-ranking, and streamed, source-cited answers from Gemini.
An advanced PostgreSQL data warehouse built on a star schema, with analytics queries, window functions, views, triggers, and query optimization work.
A data warehouse and business-intelligence project modeled on Foodpanda's operations — schema design plus analytical queries over the data.
A Django web application wrapping a computer-vision detection model with an alerting layer — the backend and application structure around a model, not just the model itself.
04 — Journey
Leveraging a strong foundation in Python and Django backend systems to build production-grade AI applications.
- Architecting and deploying production-grade Generative AI applications, focusing on RAG pipelines, custom LLM tooling, and agentic workflows.
- Integrating advanced vector databases, embedding model management, and LLMOps practices to ensure low-latency, deterministic AI outputs in production environments.
- Designed and built scalable backend web applications, REST APIs, and relational database schemas using Python and Django.
- Engineered complex PostgreSQL databases, including star-schema data warehouses, custom PL/pgSQL analytics queries, and performance-tuned database indexing
- Integrated third-party APIs, authentication systems, and cloud deployments while maintaining production software reliability and code quality.
- Maintained containerized development environments with Docker and established Git-based team workflows and code review practices.
05 — Contact
Let's Work Together