AI Engineer with 1+ years in Generative AI, RAG & Machine Learning
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AI/ML Engineer with 1+ year of experience building Generative AI, Retrieval-Augmented Generation (RAG), intelligent search, and machine learning applications using Python. Experienced in developing multi-agent workflows, semantic retrieval pipelines, and LLM-powered systems using LangChain, LangGraph, vector databases, and modern AI frameworks.
CMR College of Engineering & Technology, Hyderabad
B.Tech · Electronics & Communication Engineering
August 1, 2019 – June 30, 2023
Frandzzo Technologies Pvt Ltd
AI Engineer
March 9, 2025 – May 19, 2026
Chennai, Tamil Nadu, India
Rubixe - AI Solutions
Data Science
June 16, 2023 – January 21, 2024
Hyderābād, Telangana, India
RAG Document Intelligence Platform
May 25, 2026 – June 9, 2026
Built an end-to-end Retrieval-Augmented Generation (RAG) platform enabling PDF/TXT document ingestion, semantic retrieval, and AI-powered question answering using LangChain, ChromaDB, and Groq-hosted Llama 3.1 8B. Designed the ingestion and retrieval pipeline using RecursiveCharacterTextSplitter, FastEmbed embeddings, and ChromaDB vector search to retrieve relevant document context and generate grounded responses. Developed REST APIs with FastAPI, containerized the application using Docker, and deployed it on AWS EC2; validated the solution using enterprise documents such as Amazon Annual Reports for business insight extraction workflows.
View ProjectPPE Detection System
September 16, 2025 – May 19, 2026
Scoped data requirements; captured industrial-site images across varied lighting conditions, camera angles, and occlusion scenarios to build a robust training dataset. Annotated multi-class PPE labels (helmets, vests, gloves, goggles) using Roboflow; applied class balancing and augmentation pipelines to improve model generalization. Built a YOLOv11-based real-time PPE detection system across live video streams with an end-to-end ML pipeline, improving workflow efficiency by 30%. Boosted model accuracy by 8-12% via hyperparameter tuning and regularization.
Multi-Agent Sentiment Analysis System
March 16, 2025 – September 15, 2025
Built a multi-agent AI system to analyze employee survey feedback using LLM-powered sentiment classification and category prediction workflows. Designed AI agents for sentiment prediction and feedback categorization across employee survey responses. Developed scoring logic to calculate sentiment scores across responses and stored processed results in MySQL for reporting and analytics.
Certified Data Scientist – NASSCOM
NASSCOM
June 1, 2026 – Present
Certified Data Scientist – IABAC
IABAC
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio demonstrates a strong interest and practical experience in diverse AI/ML domains, including GenAI (RAG, multi-agent systems), NLP (sentiment analysis), and Computer Vision (PPE detection). This breadth of technical engagement, coupled with experience in both a startup (Frandzzo Technologies) and a consulting firm (Rubixe AI Solutions), suggests adaptability and a willingness to tackle varied challenges. The projects align well with the innovative and problem-solving nature often found in AI engineering roles, indicating a good cultural fit for a dynamic technical environment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to translate business problems into ML solutions and collaborate with stakeholders. The detailed descriptions of project phases (data scoping, annotation, pipeline design, deployment) suggest a structured approach to problem-solving and operational awareness. However, without direct interview data, specific soft skills like teamwork, leadership, or adaptability cannot be fully assessed.