Open to opportunities

Dipak Kumar Tomar

@dipakkumartomar

Message

Agentic GenAI engineer building reliable, cost‑efficient multi‑agent LLM systems.

India

Message

What I'm looking for

I seek roles building production GenAI/agentic systems with ownership of architecture, cloud deployment, observability, and cost/latency optimization in collaborative teams.

I am an Agentic GenAI engineer with 3+ years delivering multi‑agent LLM workflows, backend APIs, and cloud‑native production systems. I specialize in LangGraph/AutoGen/CrewAI, RAG and vector retrieval, and have productionized services on AWS using containerized deployments, CI/CD, and observability to improve reliability, latency, and cost.

I have shipped customer‑facing solutions across education, retail, and enterprise domains — from large‑scale React Native apps to orchestrated agent pipelines for document processing, insurance verification, and medical quotation automation. I focus on pragmatic engineering: robust APIs (FastAPI/Flask), async throughput, secure auth, and monitoring to ensure scalable, maintainable delivery.

Experience

Work history, roles, and key accomplishments

Current

Agentic GenAI Engineer

Current

Techype

Feb 2025 - Present (1 year 4 months)

Designed and shipped multi-agent LLM workflows for adaptive learning and document automation, optimized RAG pipelines to reduce latency and token costs, and productionized services on AWS with CI/CD and observability.

LangGraph AutoGen fastAPI Pinecone Faiss AWS Lambda Docker CI CD OpenTelemetry

React Native Developer

Campus 365 Private Limited

Feb 2023 - Jan 2025 (1 year 11 months)

Engineered a 50K+ user cross-platform app with React Native and TypeScript, integrated AI-driven personalization that increased engagement by 40%, and improved app performance by 30% while reducing production bugs via CI/CD and crash analytics.

React Native TypeScript Socket.IO SQLite Fastlane Sentry

Software Engineer

Till POS Technologies

Aug 2022 - Feb 2023 (6 months)

Built full-stack POS web app with Next.js and Node.js, implemented ML sales-forecasting that reduced stockouts by 25%, and delivered reliable payment integrations with 99.9% transaction reliability.