I build production-grade GenAI systems that actually ship — RAG pipelines serving millions of docs at sub-1s latency, LangGraph agentic workflows, Azure OpenAI integrations, and multi-agent orchestration.

With 14+ years of engineering leadership across SaaS, InsurTech, AI, and hospitality, I bring both the architectural depth and the delivery discipline your project needs.

Currently leading a 12-member cross-functional team and taking select freelance clients for ambitious AI challenges.

I always believe 💬 "If you're good at something, never do it for free." — The Joker

Built an enterprise-scale RAG pipeline serving millions of documents at sub-1s P95 latency. Hybrid retrieval with re-ranking, citation tracking, and multi-vector search using Azure AI Search + PGVector.

Azure OpenAI LangChain PGVector Azure AI Search FastAPI Docker

Hybrid semantic search combining dense + sparse vectors with OpenSearch and FAISS. Sub-second retrieval across millions of records with custom re-ranking.

FAISS OpenSearch PGVector Python FastAPI Redis

Production conversational AI platform with WebSocket streaming, session management, and context-aware multi-turn dialogue. Integrated with Jio's service automation layer.

GPT-4o FastAPI WebSockets PostgreSQL Redis Azure OpenAI

Dynavec is a production-grade vector database and retrieval engine. I contributed deep ML/IR features across quantization, hybrid search, RAG integrations, and embedding optimization.

Contributed 12+ features across quantization, hybrid search & RAG integrations:

• 🗜️ Scalar Quantization & OPQ — INT8 compression for 4× memory reduction; OPQ rotation improved Recall@10 by +1.4pp at same vector footprint
• ⚡ HNSW Hot Tier & PQ Persistence — in-process ANN search for ultra-low latency; codebook serialization for production reloads
• 🔀 Hybrid Search & Learned RRF Weights — fused graph+ANN retrieval via RRF; ML-trained BM25/vector blend weights from labeled data, zero manual tuning
• 🎯 Cross-encoder Reranker & Vectorized MMR — two-stage precision reranking; diverse result sets via Maximal Marginal Relevance
• 🔌 RAG Integrations — native connectors for Haystack, DSPy, and LlamaIndex; embedding cache to cut re-embedding latency and API cost

📈 OPQ lifted Recall@10 by +1.4pp · Scalar INT8 cut memory 4× · RRF weights learned from data, zero manual tuning

Vector Quantization HNSW ANN Search RRF Hybrid Search Cross-Encoder MMR LlamaIndex Haystack DSPy Python

LLMs & GenAI

Orchestration & Agents

Vector DBs & Search

Cloud & MLOps

Languages & Backend

2024 – Present  │  Sr. AI/ML Engineering Manager
                │  Leading 12-member team · Enterprise RAG · Agentic AI · Conversational AI
                │
2020 – 2024     │  Senior Engineering Manager / GenAI Architect
                │  InsurTech & SaaS · LLM agents · Onsite: 🇮🇩 🇻🇳 🇭🇰 🇹🇭
                │
2016 – 2020     │  Engineering Lead
                │  SaaS & Hospitality · Team scaling · Python & Ruby on Rails
                │
2015 – 2016     │  Software Developer / Programmer Analyst
                │  C2L Biz Solutions · FWD Hong Kong · Sequis
                │
2012 – 2015     │  Software Developer
                │  Sodel Solutions Pvt Ltd

🇮🇩 Indonesia C2L Biz Solutions Analysis Programmer InsurTech · Insurance Systems

🇻🇳 Vietnam C2L Biz Solutions Analysis Programmer InsurTech · Insurance Systems

🇭🇰 Hong Kong C2L Biz Solutions Analysis Programmer InsurTech · Insurance Systems

🇹🇭 Thailand C2L Biz Solutions Analysis Programmer InsurTech · Insurance Systems

Certifications

Awards

• 🚢 I ship production systems, not demos — architecture to deployment with monitoring baked in
• ⚡ Hands-on depth — I write the code, tune the prompts, and design the infra; not just slide decks
• 🔬 Open source contributor — active Dynavec contributor in vector quantization, ANN search, hybrid retrieval
• 🌏 Proven cross-border delivery — onsite in 4 countries (Indonesia, Vietnam, Hong Kong, Thailand), distributed team leadership
• 👥 People leadership — hired, mentored, and scaled teams of 12–15 engineers
• 📈 Business-driven — every system I build tied to measurable outcomes (latency, cost, effort saved)

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Got a RAG system to architect, an AI agent to ship, or a team that needs GenAI leadership? I've done it at enterprise scale. Let's talk.

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Sanket Tikhande · AI/ML Sr.Engineering Manager & GenAI Architect · Mumbai, India