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ajtiwari1/README.md
Ajay Tiwari — Generative AI Engineer

Portfolio LinkedIn Email

"The future belongs to those who can master the art of conversation with machines." Ajay Tiwari


🧬 How I Build AI Systems

Runtime RAG pipeline (webhook → condense → retrieve → rerank → generate → reply) and the release gate: component-level, end-to-end, safety and ops evals compared against a baseline

📚 RAG that earns its context

Structure aware chunking, embeddings matched to the domain, and a wide vector search narrowed by a reranker. Follow up questions are rewritten with conversation memory, and answers stay grounded or abstain instead of guessing.

🤖 Agents that ship to production

Stateful, tool using agents orchestrated as graphs and connected to real systems through MCP servers, APIs and databases. Served with FastAPI behind signed webhooks, with memory, retries and background work, on whatever channel the users already live in.

🛡️ Evaluation as a release gate

Every stage of a RAG pipeline or agent is scored in isolation with DeepEval, then end to end with LLM as a judge rubrics. Regressions fail the release. In production, LangSmith scores live traces continuously and alerts on any metric drop.

🛠️ Tech Stack

🧠 Agents & Frameworks

MCP LangGraph LangChain Semantic Kernel
🔮 Models & Providers

OpenAI Anthropic Hugging Face OpenRouter Groq Ollama
📚 RAG & Vector Stores

Pinecone ChromaDB FAISS Weaviate Qdrant
🛡️ Eval, Guardrails & Observability

DeepEval LLM-as-a-Judge Custom Guardrails LangSmith
⚙️ Backend & APIs

Python FastAPI Pydantic Flask JWT
🗄️ Data

PostgreSQL MySQL SQLite Redis Pandas NumPy scikit-learn
☁️ Cloud & DevOps

Azure Docker Podman Linux VPS DigitalOcean Vercel Railway Git Postman

📊 GitHub Activity

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