Generative AI Engineer with less than a year in RAG pipelines and NLP automation.
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Associate Software Engineer Intern at Mphasis with hands-on experience in Generative AI, Prompt Engineering, and NLP automation. Built RAG pipelines using LangChain/LangGraph, achieving up to 0.85 answer relevancy and reducing manual analysis by ~85%. Optimized prompts to improve response quality by 12% and enhanced data reliability by ~30% through HuggingFace-based preprocessing. Skilled in deploying ML workflows with FastAPI, Docker, and AWS, delivering production-ready AI solutions aligned with enterprise automation needs.
Lovely Professional University
Bachelor of Technology · Electronic and Communication (Minor in Data Science)
August 1, 2022 – May 1, 2026
Mphasis
Associate Software Engineer (Internship)
January 1, 2026 – April 1, 2026
India
IndiaDigest AI-Powered News Intelligence Platform
June 1, 2026 – Present
Built end-to-end NLP pipeline for news analysis – processed 100+ weekly articles and 4K+ user comments using text cleaning, tokenization, chunking, and embedding generation (Hugging Face sentence-transformers); stored vectors in FAISS for semantic retrieval. Integrated Llama 3.1 with retrieval-augmented generation workflow – reduced manual content analysis effort by 55% through automated summarization and intelligent question-answering over document corpus. Evaluated LLM output quality using RAGAS metrics (answer relevancy: 0.82, context precision: 0.78) – optimized chunking strategies and few-shot prompts to improve response grounding and factual accuracy.
View ProjectAdaptive RAG - Agentic AI Chatbot
June 1, 2026 – Present
Prototyped multi-agent RAG system using LangChain and LangGraph – built query router to classify user intent across three specialized agents (vector retrieval, LLM generation, web search); evaluated response relevance and retrieval quality on 200+ document corpus. Engineered self-correcting evaluation workflow – implemented document relevance grading and automatic query rewriting when retrieval confidence falls below threshold; improved retrieval accuracy by 30% over single-pass baseline. Deployed production-ready inference API using FastAPI and Docker – implemented structured output parsing with Pydantic validation; documented agent workflows for reproducibility and team handoff.
View ProjectSolved 200+ problems
LeetCode
June 1, 2026 – Present
Develop Generative AI Applications
IBM Coursera
June 1, 2026 – Present
Infineon Hackathon 2025 Finalist (Top 5%): Delivered TabuQuest an AI/ML tabular data querying and intelligent answering.
Infineon
January 1, 2025 – Present
Cultural Fit Analysis
The candidate's involvement in personal projects and a hackathon, alongside an internship, demonstrates initiative and a proactive learning attitude. The focus on Generative AI and NLP aligns well with the target role. The breadth of skills across programming, AI/NLP, evaluation, ML, and deployment indicates a versatile individual who can adapt to different aspects of an AI engineering role. The LeetCode achievement suggests a commitment to continuous improvement and problem-solving, which are positive cultural indicators.
Soft Skills & Operational Fit
The candidate's project descriptions highlight problem-solving, optimization, and systematic evaluation, suggesting a strong analytical mindset and attention to detail. The experience in managing prompt iterations and debugging workflows indicates a methodical approach to development. The documentation of agent workflows points to good organizational and collaboration skills. The hackathon achievement suggests innovation and ability to deliver under pressure.