I'm Amey Narwadkar, a Master's student in Scientific Computing at Heidelberg University and a Working Student at NEC Laboratories Europe.
I work on machine learning systems, with a particular interest in:
- LLM & RAG systems
- Multi-agent AI
- Knowledge graphs
- Efficient NLP
- Applied machine learning
My background in mathematics shapes how I approach ML: understand the problem deeply, build the simplest system that works, and evaluate it properly.
Currently exploring opportunities in AI / ML Engineering and Applied AI.
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Python |
PyTorch |
FastAPI |
Docker |
Kubernetes |
GCP |
Linux |
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PostgreSQL |
Redis |
React |
Next.js |
TypeScript |
Git |
Bash |
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OpenCV |
sklearn |
R |
HTML |
CSS |
Supabase |
VS Code |
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Production-grade, end-to-end RAG system featuring a two-stage retrieval funnel (hybrid search and cross-encoder reranking), conversation history rewriting, semantic caching, and verifier-backed citation constraints.
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A 7-agent system orchestrated via Google ADK that searches ArXiv, PubMed, and OpenAlex. It clusters papers, rigorously assesses evidence quality, and synthesizes publication-ready literature reviews.
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Implemented early-exit strategies for BERT using entropy, margin, and patience-based halting. Integrated micro self-verification and conducted latency & calibration analysis.
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End-to-end tennis video analysis pipeline featuring YOLO-based player detection, CNN ball tracking, court keypoint estimation, real-time speed metrics, and a dynamic mini-court visualization.
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Building autonomous agent teams that collaborate to solve complex tasks, from research synthesis to automated workflows. |
Production-ready retrieval pipelines with hybrid search, reranking, structured outputs, and rigorous evaluation. |
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End-to-end ML applications: computer vision, conversational AI, sentiment analysis, and automation pipelines. |
Making language models faster and cheaper with early-exit inference, calibration, and compute-aware optimization. |
