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I’m an AI/ML Engineer based in Germany, focused on building production-grade AI systems that teams can trust: reproducible experiments, measurable quality, robust deployment, and clear monitoring.
Core strengths
- AI/ML Engineering: end-to-end pipelines, evaluation, deployment, monitoring
- LLM systems (RAG + Agents): retrieval quality, grounded responses, tool-using workflows, guardrails
- Multimodal CV: representation learning, practical pipelines for perception tasks
- MLOps / LLMOps: CI/CD, containers, infra as code, monitoring and iteration loops
| Project | What it is | Stack | Link |
|---|---|---|---|
| 🔎 Technical Content Writer Agent | Streamlit + LangGraph application for generating technical blog posts from a short prompt or topic. | Streamlit, LangGraph, Python | https://github.com/SurajBhar/deep-blog-agent |
| 🔎 Personal Assistant (Strands Multi-Agent) | AI Agent "agent-as-tool" pattern | Sqlite, AWS, Strands, Perplexity MCP Server, Amazon Bedrock, Python | https://github.com/SurajBhar/personal_assistant_agent |
| 🔎 RAG on Azure (FastAPI) | Practical RAG service design for grounded answers | FastAPI, Azure, Search/RAG | https://github.com/BharAI-Lab/rag_azure_fastapi |
| 🧠 RAG with NVIDIA NIM | Lightweight doc-chat app workflow | NIM, Python, RAG | https://github.com/SurajBhar/rag_nim |
| 📊 Tabular ML Prediction | Pipeline: prep → modeling → evaluation | Python, scikit-learn | https://github.com/SurajBhar/hrprediction |
| 🎬 Movie Sentiment Prediction | Text classification with clear preprocessing + eval | NLP, Python | https://github.com/SurajBhar/moviesentiment |





