AI Engineer | Software Engineer

I build production-grade AI systems with a focus on
AI agents and real-time AI applications.

• 🔹 AI Engineer with strong SWE foundation (backend + full-stack)
• 🔹 Building LLM-powered agent systems (LangGraph, tool-based workflows)
• 🔹 Focused on LLMOps, RAG pipelines, and scalable AI infrastructure
• 🔹 Interested in latency optimization, cost efficiency, and reliability in production AI

• LangGraph (multi-agent orchestration)
• OpenAI / OpenRouter APIs
• RAG (semantic + schema-aware retrieval)
• Milvus (Vector DB)
• Prompt Engineering & Tool Calling

• FastAPI (async, SSE streaming)
• Python
• SQLAlchemy
• PostgreSQL
• REST APIs

• React / Next.js
• Tailwind CSS

• Docker / Docker Compose
• AWS (EC2)
• Bitbucket Pipelines (CI/CD)
• PM2
• SonarCloud

• SAP HANA
• Salesforce
• SharePoint
• ServiceNow

Production-grade multi-agent system integrating enterprise HR tools

Key Highlights

• ⚡ Built LangGraph-based multi-agent system (supervisor–worker architecture)
• 🔗 Integrated enterprise tools via MCP server with RBAC & audit trails
• 🧠 Implemented schema-aware RAG for structured + unstructured data retrieval
• 📡 Designed real-time streaming (SSE) for interactive workflows
• 🚀 Reduced token usage by 75% and p95 latency by 80% with hybrid orchestration

Tech Used FastAPI LangGraph Milvus PostgreSQL AWS Docker SSE

• ⚡ Efficient LLM systems (latency / throughput / KV cache optimization)
• 🧠 Intelligent agent workflows (planning + tool use)
• 🏗️ Production-ready AI (observability, scaling, reliability)
• 💰 Cost optimization in LLM applications

• 💼 Open to AI Engineer / SWE opportunities
• 🌱 Always learning about LLMOps, vLLM, and distributed inference

⭐️ “Turning LLMs into reliable, scalable systems.”