Market Intelligence GenAI Assistant
Analyzes large pharmaceutical market research reports to surface drug demand trends, competitor activity, and importer data. Cut manual analysis time by ~70%.
// hello world, I'm
AI/ML Engineer — LangGraph, MCP Servers, RAG Pipelines
I build production AI systems — not demos.
Agentic pipelines, MCP servers, finetuned models.
AI/ML Engineer focused on building production GenAI systems that do real work — multi-agent pipelines, RAG over enterprise data, and MCP servers that connect AI tools to existing infrastructure. Currently at Matchbest Group.
Previously built pharma AI systems at Hayai Pharma — cutting manual analysis from hours to minutes using LangGraph-orchestrated agents and vector-retrieval pipelines on AWS. I specialise in Spec-Driven Development, AI Orchestration, and Claude Code Subagents.
from sanchit import Engineer
e = Engineer(
frameworks = ["LangGraph", "LangChain"],
models = ["Hugging Face", "AWS Bedrock"],
vector_dbs = ["Pinecone", "FAISS"],
serving = "FastAPI", # always prod-ready
infra = ["AWS", "Docker", "n8n"],
specialty = "MCP Servers", # Claude ↔ enterprise
)
e.deploy() # ships to prod, not sandboxAnalyzes large pharmaceutical market research reports to surface drug demand trends, competitor activity, and importer data. Cut manual analysis time by ~70%.
Semantic search and LLM reasoning over pharma regulatory documents. Enables natural-language queries with grounded citations — no manual lookup.
Parallelised LangGraph pipeline transforming raw drug specs into validated packaging content — dosage tables, warnings, storage instructions — with cost-aware model routing.
Matchbest Group · Gurugram
Hayai Pharma Private Limited · Meerut
Family-Owned Trading Enterprise · Meerut
Rubico · Haridwar
Agentic AI with LangGraph, CrewAI, AutoGen & BeeAI
Advanced RAG with Vector Databases & Retrievers
Google Data Analytics Specialization
Foundations of AI and Machine Learning
🎓 MCA + BCA — Graphic Era Deemed to be University (2013 – 2018)