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Generative AI Engineer with less than a year in LLM & RAG
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
MCA graduate and Generative AI Engineer with hands-on experience building LLM-powered applications using LangChain, RAG architectures, and vector databases. Skilled in Python, FastAPI, and Docker for designing and deploying AI-driven backend systems. Experienced in prompt engineering, semantic search, and integrating large language models into production-style chatbots and conversational agents. Strong analytical thinking with a fast learning curve for emerging AI tools and frameworks.
Anna University
Master of Computer Applications
August 1, 2024 – June 30, 2026
Annamalai University
Bachelor of Computer Applications
August 1, 2021 – June 30, 2024
Tech Mahindra
AI/ML Intern
January 1, 2026 – March 1, 2026
Chennai, Tamil Nadu, India
Cyber Law Assistant Chatbot
January 1, 2026 – June 1, 2026
Engineered an AI-powered legal assistant chatbot using an LLM and RAG pipeline, enabling accurate responses for 100+ cyber law queries through improved semantic search and document chunking. Designed the retrieval pipeline with FAISS vector indexing and HuggingFace sentence-transformer embeddings for fast, relevant context retrieval. Built a multilingual frontend (English, Tamil, Tanglish) to broaden accessibility of AI-generated legal guidance.
Crop Recommendation System
January 1, 2026 – June 1, 2026
Developed a machine learning pipeline using Random Forest on agricultural datasets, processing soil nutrients, temperature, and rainfall data to generate precise crop recommendations via a Flask API.
Generative AI for Beginners
Unknown
June 1, 2026 – Present
Python Certification
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects show a diverse interest in applying AI to different domains (legal, agriculture), indicating adaptability and a willingness to explore various challenges. The focus on building practical, deployable solutions aligns with a results-oriented culture. However, the limited professional experience (internship only) and lack of explicit team collaboration examples in the provided data make it difficult to fully assess cultural fit beyond project diversity.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving and analytical thinking skills, which are crucial for a Generative AI Engineer role. Their experience in containerization with Docker suggests an understanding of operational best practices for deployment. The multilingual frontend project indicates an awareness of user accessibility and broader impact.