"Models are cheap. Engineering is rare."
I build AI systems that survive the transition from a notebook to production. My focus is on multi-agent orchestration (LangGraph), automated evaluation (Ragas), and secure, cost-effective deployment.
- 0.72 → 0.87: Faithfulness lift on Legal RAG through hybrid retrieval and reranking.
- 0.09%: Parameters trained to achieve domain-specific fine-tuning via QLoRA.
- 28%: Precision improvement in context retrieval using RRF (Reciprocal Rank Fusion).
- Zero GPU: Production deployment of a multi-stage legal agent on CPU-only infrastructure.
Commissioned for Indian District Courts
- The Challenge: Processing massive volumes of bilingual (Hindi/English) legal filings with strict PII compliance.
- The Solution: A 7-node LangGraph workflow featuring IndicTrans2 for translation and RAPTOR for hierarchical recursive chunking.
- Compliance: Integrated Microsoft Presidio to mask sensitive identifiers (Names, Aadhaar, Litigant details) before model ingestion to meet legal requirements.
- Tech:
LangGraph,Neo4j,ChromaDB,Redis,FastAPI,Gradio.
- The Challenge: Automating the end-to-end resolution of GitHub issues while ensuring code safety.
- The Solution: A multi-agent system (Research → Coder → Tester → PR Writer) that resolves issues in a Docker-sandboxed environment.
- Safety: Zero-network sandbox with resource caps ensures no unsafe code execution on host systems.
- Tech:
Gemini 2.0,LangGraph,Docker,LangSmith.
- The Challenge: Catching quality degradation in production before users do.
- The Solution: A production-ready monitoring system using Medallion Architecture on Delta Lake to ingest completions in real-time.
- Key Feature: Automated MLflow alerts that trigger when faithfulness or context recall drifts >0.05 from the rolling baseline.
- Tech:
PySpark,Delta Lake,Ragas,MLflow,Groq.
| Category | Tools & Frameworks |
|---|---|
| GenAI & Orchestration | LangChain, LangGraph, Multi-Agent Systems, Agentic Workflows |
| RAG & Retrieval | Hybrid Search (BM25 + Vector), RRF, CrossEncoders, Semantic Chunking |
| LLMOps & Eval | Ragas, LangSmith, MLflow, Weights & Biases, Delta Lake |
| Fine-Tuning | PEFT, LoRA/QLoRA, Unsloth, Hugging Face Transformers |
| Vector/Graph DBs | ChromaDB, FAISS, Neo4j, Redis |
| Backend & Core | FastAPI, Docker, GitHub Actions (CI/CD), Python, SQL, TypeScript |
- M.Sc. Data Science | IIIT Lucknow (Expected June 2026)
- Graduate Teaching Assistant | IIIT Lucknow (Mathematics Dept)
- B.Sc. (Hons) Mathematics | University of Delhi (2021 – 2024)
Actively seeking remote AI Engineer roles.
📫 pratyayrajak18@gmail.com