I bring scientific rigor to production AI systems. With 11+ years of experience handling complex datasets and building machine learning models, I design backend architectures and AI workflows built to run at scale.
- AI Engineering: LLM Applications, Production RAG Systems, AI Agents, LangChain, Framework-Free Execution Loops, Prompt Engineering, Structured Outputs (Pydantic), Vector Databases (pgvector, FAISS), LLM evals
- Machine Learning & Evaluation: Deep Neural Networks, Tree-Based Ensembles (XGBoost, Random Forest), Ensemble Stacking, Bayesian Inference, Nested Cross-Validation, Hyperparameter Optimization, Explainable AI (XAI)
- Infrastructure & Backend: FastAPI, PostgreSQL, Celery, Azure, Docker, Cloud Deployments, Workflow and Cluster Managers (Nextflow and Slurm), Git/GitHub Actions
- Scientific Computing: Python, PySpark, Numpy, Pandas, R, Bash, Parallel and Distributed Processing
- Production Agent Workflows: Event-driven, containerized microservices for processing unstructured documents.
- Distributed Scientific Pipelines: Parallel and distributed architectures built with PySpark and multiprocessing to handle hundreds of gigabytes of high-dimensional data.

