Anant Patankar · India
ML/AI Engineer — Agentic Systems & Generative AI
Multi-agent orchestration (Google ADK) · MCP Protocol · Production RAG · MLOps at scale
Forensic scientist turned ML engineer — I bring evidence-grade rigor to agentic AI and grounded retrieval.
What I do
Agentic AI
Multi-agent orchestration with Google ADK, MCP protocol servers, and LLM-driven reasoning & tool use — systems where the model decides, and the architecture keeps it safe, cheap, and auditable.
Production RAG
Grounded generation with source traceability, hybrid dense+sparse retrieval, cross-encoder reranking — measured with real recall and latency numbers, not vibes.
Production Systems & MLOps
Async Python, distributed pipelines (Celery/Redis/Cloud Run), containerized deploys, experiment tracking and model registries — the backbone that makes the AI shippable.
Skills
Tech Stack
01 / Agentic AI & LLMs
02 / Vector Search & Retrieval
03 / ML / Deep Learning
04 / Distributed Systems & Infra
05 / MLOps & Cloud
06 / Data Engineering
Selected work
Featured Projects
Five systems spanning agentic engines, MCP servers, and RAG pipelines. Two are open source and independently verifiable.
FLAME — Agentic Recommendation Engine
Google ADK · Gemini 2.5 Flash · GCP · FlagshipAI backend for a gamified crowd-labelling platform that turns plant workers into a distributed workforce annotating workplace-safety imagery through four mini-games. Fully LLM-driven topic selection over structured Firestore tools — no hardcoded routing — plus consensus, Elo ranking, trust scoring, and anti-gaming.
12 agents · 5 ADK skills · designed for 100K+ concurrent users & 50K+ images/day
AI-Powered HSE Training Content Generation
Vertex AI Agent Engine · RAG · Human-in-the-loop14-agent content-generation pipeline that turns safety documents into reviewed microlearning — grounded RAG with per-question source attribution and a structural guarantee that nothing publishes without human approval.
14 agents · multi-format RAG ingestion · zero auto-publish path
Lumenore Analytics MCP Server
MCP Protocol · Async PythonProduction MCP server exposing a BI platform’s analytics to any AI assistant — natural-language querying, forecasting, correlations, outlier detection — over typed, JWT-scoped, SSE-streaming tools.
sub-50ms P95 · engineered for 100+ concurrent connections · 8 analytics tools
MLOps MCP Server
MCP Protocol · MLOps · Personal OSSOpen-source MCP server giving AI assistants tool-based access to MLOps workflows — experiment tracking, model registry, dataset drift detection, pipeline DAGs, lineage tracing — wrapping MLflow, DVC, and Git.
pip install mlops-mcp-server · v0.1.0 · MIT · two-tier tool registry
Enterprise Knowledge Q&A
RAG · Hybrid Search · EvaluationRAG system over enterprise document libraries: ChromaDB + Elasticsearch hybrid retrieval, cross-encoder reranking, and token-budgeted prompts with mandatory citations — turning ~15-minute manual document hunts into sub-minute answers in internal pilot use.
85%+ recall@5 on 200 labelled queries · hybrid retrieval lifted a 65–70% dense-only baseline
Verifiable proof
Open Source
Don’t take my word for it — read the code.
mlops-mcp-server
PyPIMCP server for MLOps workflows: experiment tracking, model registry, KS-test drift detection, DAG cycle detection (Kahn’s algorithm), BFS lineage tracing with Mermaid visualizations.
lumenore-mcp
Org repoProduction analytics MCP server I authored at Netlink — FastMCP, async Python, JWT auth, SSE streaming, zero data storage. Published open source under the Lumenore Platform org.
Track record
Experience
Architected the agentic AI backend (FLAME) for an enterprise HSE gamification platform and a 14-agent training-content generation pipeline — Google ADK, Vertex AI, Gemini, Cloud Run. Led technical direction for a junior engineer; presented vector-DB trade-off analysis to senior stakeholders.
Built the open-source Lumenore Analytics MCP Server; an AI customer-support chatbot with Qdrant hybrid search + cross-encoder reranking (85%+ retrieval accuracy, sub-200ms P95 at 10K+ daily messages); and an agentic MCP client/orchestrator managing multiple MCP servers across 4 transport protocols with RBAC.
ML/GenAI work on an Enterprise Knowledge Q&A RAG system (85%+ recall@5 on labelled queries); code-analysis AI processing 500K+ lines across 10+ languages; 30+ ML/DL solutions for a national rural-development bank on 10M+ records. 2× Game Changer of the Month (Sept & Dec 2023).
Time-series forecasting (RF, SVM, LSTM) for chemical-reactor optimization with feature engineering and anomaly detection.
ML forecasting pipeline (ARIMA, RF, LSTM) and a real-time object-detection system processing 15 FPS video with TensorFlow.
About
From forensic science to agentic AI
I hold an M.Sc. in Forensic Science and moved into machine learning through self-directed study, building toward production ML and AI over nearly six years. The analytical discipline transferred directly: I design agentic and RAG systems with an emphasis on auditability, grounding, and measurable outcomes.
My current focus is multi-agent orchestration (Google ADK), MCP-based tooling, and production RAG, supported by distributed-systems and MLOps practices — with two open-source MCP servers published publicly.
Get in touch
Contact
Open to conversations about agentic AI, GenAI systems, and ML engineering roles.