Headline

AI Engineer @ IBM | GenAI • LLMs • Agentic AI • RAG • LangGraph • Python • FastAPI | Building Production AI Systems

About

I'm an AI Engineer at IBM with 3 years of experience building production-grade AI applications powered by Large Language Models, Agentic AI, Retrieval-Augmented Generation (RAG), and scalable backend systems. My work focuses on designing intelligent systems that move beyond experimentation into reliable, enterprise-ready products. I've developed multi-agent AI workflows using LangGraph, built scalable RAG pipelines over enterprise knowledge bases, optimized retrieval performance, and engineered low-latency AI services using Python and FastAPI. Highlights • Built production-ready multi-agent AI systems using LangGraph • Designed scalable RAG pipelines across 100K+ enterprise documents • Improved retrieval accuracy from 67% → 82% • Reduced LLM inference cost by 15% • Developed AI backend services using Python, FastAPI and REST APIs Technical Expertise Generative AI LLMs • Agentic AI • LangGraph • LangChain • RAG • Prompt Engineering • MCP • A2A Backend Python • FastAPI • REST APIs • Docker Vector Databases FAISS • Milvus • Elasticsearch Machine Learning Scikit-learn • XGBoost • PyTorch Data SQL • Pandas • Feature Engineering I'm passionate about building scalable AI systems that solve real business problems and am always interested in opportunities involving AI Engineering, Backend Engineering, Agentic AI, and LLM-powered applications.

Location

Bengaluru

AI stack

antigravity

claude code

ollama

ChatGPT

Google ADK

Milvus

Work experience

Artificial Intelligence Engineer at IBM, May 2024 - Present · 2 y 4 mo, Bengaluru, Karnataka, India, Engineered a reusable Python framework using LangGraph and A2A for building standardized multi-agent AI applications, contributed through IBM Innersource and published as an internal Artifactory package. Developed enterprise-grade RAG pipelines integrating LLMs, embeddings, vector databases and query decomposition for efficient information retrieval. Built LangGraph-based agentic workflows supporting multi-step reasoning, tool orchestration and automated task execution for enterprise AI applications. Improved RAG retrieval accuracy from 67% to 82% through embedding optimization and query decomposition. Reduced LLM inference costs by 15% through prompt compression and context optimization. Implemented LLM observability using Langfuse for tracing, monitoring, debugging and analyzing agent workflows. Developed scalable Python and FastAPI backend services and REST APIs to integrate AI capabilities into enterprise applications. Integrated A2A-based agent communication to support interactions between multiple AI agents. Collaborated with cross-functional engineering teams to develop, enhance and maintain enterprise-grade GenAI and multi-agent AI solutions.

AI/ML Engineer at IBM, Jul 2023 - Apr 2024 · 9 mo, Bengaluru, Karnataka, India, Developed an XGBoost-based pricing prediction model, improving prediction accuracy by 30% across four or more global regions. Built a RAG-based enterprise question-answering solution using LangChain and Milvus, reducing query resolution time from 5 minutes to 15 seconds. Implemented machine learning workflows for automated pricing and enterprise business applications. Developed ReactJS UI components and integrated them with backend APIs to support pricing and quotation management. Collaborated with product managers and engineering teams to translate business requirements into deployable machine learning solutions.

Software Developer Internship at IBM, Jan 2023 - Jun 2023 · 5 mo, Bengaluru, Karnataka, India, Developed frontend components using ReactJS and integrated them with backend services. Worked with JavaScript, REST APIs and Spring Boot to support full-stack application development. Contributed to application development, debugging and feature enhancements within the engineering team.

Education

Bachelor of Engineering - BE, Electronic and communication engineering, Kongu Engineering college