// AI Automation & Agentic Systems Engineer
Building systems that think, decide & act autonomously
I design Agentic AI, multi-agent pipelines, LLM-powered automation, and RAG architectures that connect AI to real engineering workflows — from Jira ticket routing and CI/CD orchestration to Graph RAG knowledge systems.
// who I am
Engineer at the intersection of AI systems and real-world automation
I'm an AI Systems Engineer based out of India, currently building multi-agent automation at EPAM Systems. My work sits at the edge of LLM orchestration and practical engineering — I care deeply about systems that don't just generate text, but actually do things.
My journey started in full-stack engineering, moved through GenAI product development at Infosys, and has now evolved into designing agentic workflows with Human-in-the-Loop controls, Temporal orchestration, and LLM-driven decision engines.
I believe the most interesting AI work happens when intelligent systems interact with messy real-world infrastructure — Jira tickets, Jenkins pipelines, CI/CD systems, and production databases. That's where I operate.
Outside of work, I explore Graph RAG architectures, build open-source AI projects, and experiment with local models using Ollama and LangGraph.
// career
From full-stack engineering to LLM-powered agentic systems
// capabilities
From LLM orchestration to cloud deployments
// builds
Personal experiments at the frontier of AI, agents, and full-stack engineering
Enterprise-grade Agentic AI & AIOps platform that empowers software engineers, DevOps engineers, SREs, and AI engineers through repository intelligence, engineering knowledge retrieval, deployment planning, incident investigation, and AI-assisted operational decision-making. Built around a planner-centric multi-agent architecture using LangGraph, Hybrid RAG, semantic memory, PostgreSQL (pgvector), MCP tool orchestration, and Human-in-the-Loop (HITL) approvals for intelligent engineering workflows.
Real-time entity extraction using spaCy feeds a dynamic Neo4j knowledge graph for relationship-aware Graph RAG querying. Integrates LangChain Text2SQL agents for natural language queries against auto-generated SQLite databases from uploaded documents. Combines graph traversal with vector similarity search using Ollama embeddings and FAISS.
Multi-agent system powered by LangGraph and a custom MCP server that routes user requests to specialized agents based on intent. Runs locally using Llama 3.2 through Ollama, demonstrating production-style agent orchestration, intelligent tool routing, and context-aware decision making without relying on cloud-hosted LLMs.
Full-stack Generative AI application using GPT-2 models hosted through Hugging Face for context-aware text generation. Supports customizable prompts, tone and length controls, with a React frontend and Node.js backend managing model inference, API integration, and generation workflows.
// background
Academic foundation in engineering
// credentials
Verified expertise across AI, cloud, and graph platforms