---
title: "AKHIL BABU NAMBUR - Full Stack LLM Development Associate in Bengaluru | Tal"
description: "Meet AKHIL BABU NAMBUR, 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 based in Bengaluru. AI stack includes claude code, claude, and codex. Explore public work l..."
image: "https://tal.club/meet/nambur-akhil-babu/share-image"
---

# AKHIL BABU NAMBUR on Tal

## 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

[View the original profile](https://tal.club/meet/nambur-akhil-babu)

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