Headline

Full Stack AI Engineer | 2+ Years Hands-On Experience | Generative AI | LLM | Agentic AI | RAG | Multi-Agent Systems | NLP | Computer Vision | Python | LangChain | LangGraph | FastAPI

About

Full Stack AI Engineer with 2+ years of hands-on experience building production-ready AI, LLM, NLP, computer vision, and backend engineering solutions. Currently working at Accenture, specializing in Generative AI, Large Language Models (LLMs), Agentic AI, Multi-Agent Systems, Retrieval-Augmented Generation (RAG), NLP, Computer Vision, and scalable backend development. Experienced in designing AI-powered automation systems that improve operational efficiency, reduce manual effort, and deliver context-aware intelligent workflows. Hands-on expertise includes: • Generative AI, LLMs, Agentic AI & Multi-Agent Systems • Retrieval-Augmented Generation (RAG), Vector Databases & Embeddings • LangChain, LangGraph, Hugging Face Transformers & Prompt Engineering • NLP, spaCy, NER, Text Processing & Document Intelligence • Computer Vision, OpenCV, YOLOv7, SAM, VGG & Image Segmentation • Python, FastAPI, Flask & REST API Development • SQL, PostgreSQL, MongoDB, ChromaDB, NetworkX & SQLAlchemy • Docker, Git, Linux, Gradio, PyInstaller & Model Deployment At Accenture, I have developed AI-driven engineering solutions including multi-agent AI systems, document automation platforms, technical drawing comparison pipelines, STE validation systems, LLM-assisted authoring workflows, and intelligent document processing solutions. Key impact includes reducing manual inspection time by 70%, reducing document review time by 60%, automating approximately 80% of manual authoring effort, reducing false positives by 30%, and improving workflow efficiency through agentic AI and RAG-based systems. I also build independent AI projects focused on local-first multi-agent assistants and enterprise NLP-to-SQL systems, with emphasis on tool orchestration, schema reasoning, semantic caching, secure SQL generation, and real-time AI execution. Published in an SCI-indexed journal and named as an inventor on a WIPO-granted patent, with a strong interest in applied AI research, intelligent automation, and production-grade AI engineering. Core Focus: AI Engineering | Generative AI | LLM Applications | Agentic AI | Multi-Agent Systems | RAG | NLP | Computer Vision | Backend Engineering | AI Automation

Location

Bengaluru

AI stack

claude code

claude

codex

deepmind

elevenlabs

github copilot

Work experience

Full Stack LLM Development Associate at Accenture, Sep 2024 - Present · 2 yrs 3 mos, Bengaluru, Karnataka, India, Full Stack LLM Development Associate Accenture | Bengaluru, Karnataka September 2024 – Present • Develop production-ready Generative AI, LLM, NLP, Computer Vision, and intelligent automation solutions for engineering workflows. • Built an Engineering Drawing Comparison pipeline using OpenCV and NumPy to detect added, removed, and shifted changes, reducing manual inspection time by 70%. • Developed an NLP-driven Concession Segregation Tool using PyMuPDF for structured extraction across 100+ documents, with a Gradio interface that reduced review time by 60%. • Developed an LLM-assisted Authoring Workflow Procedure Generation system using transformer-based inference and callout equivalence mapping, automating ~80% of manual authoring effort. • Enhanced the TWEET – STE Validation Tool using spaCy NLP pipelines and ASD-STE100-compliant validation logic, reducing false positives by 30%. • Automated torque-value extraction from engineering documents using Regex and NLP, mapping results to Bills of Materials with 100% traceability. • Built an Agentic AI Engineering Console using LangChain and LangGraph with a Supervisor Agent routing queries to specialized AI agents, reducing manual tool-switching by 40%. • Engineered 5+ modular AI agents for drawing comparison, concession segregation, STE validation, authoring workflows, and torque extraction. • Integrated RAG and vector databases for context-aware responses, improving answer relevance by 30%. • Applied advanced prompt engineering to optimize domain-specific LLM outputs and reduce hallucinations by 25%. Technologies: Python, LangChain, LangGraph, LLMs, Generative AI, Agentic AI, Multi-Agent Systems, RAG, NLP, spaCy, OpenCV, NumPy, PyMuPDF, Gradio, Regex, Vector Databases, Prompt Engineering

Industry X Intern at Accenture, Feb 2024 - Jun 2024 · 5 mos, Bengaluru, Karnataka, India, • Developed a Computer Vision pipeline for technical illustration analysis using image segmentation and VGG-based deep learning feature extraction. • Integrated Segment Anything Model (SAM) for image segmentation and Mistral LLM with structured prompting for LLM-driven similarity analysis. • Improved technical illustration comparison accuracy by 25% over the baseline through the integration of Computer Vision and LLM-based analysis. Technologies: Python, Computer Vision, Deep Learning, VGG, SAM, Mistral LLM, Image Segmentation, Prompt Engineering

Education

Bachelor of Technology - BTech, Computer Science, Velagapudi Ramakrishna Siddhartha Engineering College

Public links

github: https://github.com/namburakhilbabu-alt

youtube.com: https://youtube.com/@https://youtube.com/@aivify_studios?si=u8bzIRRFQ1hhuZ_5