Help & Supportcareers@gravityer.com
Generative AI Engineer with less than a year in RAG pipelines and NLP automation.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
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
Adaptive 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 ProjectIndiaDigest 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 ProjectDevelop Generative AI Applications
IBM Coursera
June 1, 2026 – Present
Solved 200+ problems
LeetCode
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.