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AI Engineer with less than a year in Agentic AI Systems & RAG Pipelines.
Final-year CS engineer with hands-on experience building agentic AI systems - including a Model Context Protocol (MCP) server enabling autonomous tool execution by AI agents, and a RAG pipeline grounding natural language queries in live data. Built TraceLab, a healthcare-compliance AI platform validated against FDA 21 CFR Part 11 and ISO 13485 standards - direct experience in the regulated life-sciences domain. Strong Python fundamentals, hands-on LLM API integration (Google Gemini, NVIDIA NIM), and familiarity with cloud platforms (GCP, Azure). Quick learner with an ownership-driven mindset, ready to contribute to agentic supply chain orchestration in the life sciences industry.
Nutan College of Engineering and Research
B.Tech · Computer Science and Engineering (AI Specialization)
August 1, 2022 – June 30, 2026
New Arts, Commerce and Science College
HSC · Science
June 1, 2021 – May 31, 2022
Don Bosco English Medium School
SSC
June 1, 2020 – May 31, 2020
KisanSahay - Agricultural Scheme Access Platform
January 1, 2026 – January 1, 2026
Designed a deterministic decision engine with explicit guardrails – deliberately avoiding LLM-based decisions for high-stakes eligibility outcomes, demonstrating practical understanding of where agentic/LLM systems need hard constraints to prevent unreliable outputs Built a full-stack system with offline-first resilience proactive failure handling and graceful degradation, a core requirement for production-grade agentic systems operating in unreliable environments
View ProjectTraceLab - Medical Device Software Compliance Platform
December 1, 2025 – January 1, 2026
Built an AI-powered compliance platform for the regulated healthcare/life-sciences domain – orchestrating Google Gemini and a local MedGemma model to automatically extract and validate requirements against FDA 21 CFR Part 11 and ISO 13485 standards Designed an automated, agent-style pipeline that extracts requirements, generates test cases, executes them, and produces a traceability matrix – without manual intervention at any step, directly demonstrating end-to-end agentic workflow design in a regulated industry Designed an intelligent traceability matrix mapping generated test cases back to source code and compliance documents - significantly reducing manual QA overhead; deployed live at trace-lab.vercel.app
CodeGuardian – AI-Powered Institutional Memory System
August 1, 2025 – June 1, 2026
Built a production agentic AI system – implemented a Model Context Protocol (MCP) server exposing 9 autonomous tools (dependency analysis, compliance scanning, test generation) that AI agents like Claude Code and GitHub Copilot invoke independently to complete multi-step tasks without human intervention Engineered a RAG pipeline using NVIDIA NIM embeddings and a ChromaDB vector store - grounding agent responses in live, retrieved context to eliminate hallucinations, a core requirement for reliable agentic systems Designed the tool orchestration layer connecting multiple specialized tools behind a single agent-accessible interface - directly analogous to multi-agent workflow orchestration in enterprise systems Applied prompt engineering for engineering productivity – using Claude Code and GitHub Copilot to accelerate implementation of the RAG pipeline and MCP server while maintaining full architectural ownership and catching AI-generated code errors
View ProjectIntroduction to Programming with Python
Harvard CS50
June 1, 2026 – Present
Machine Learning I
Columbia
June 1, 2026 – Present
Data Science
Infosys
June 1, 2026 – Present
Introduction to AI & Machine Learning
Harvard CS50
June 1, 2026 – Present
Introduction to Databases with SQL
Harvard CS50
June 1, 2026 – Present
Natural Language Processing
Infosys
June 1, 2026 – Present
Cloud Computing
NPTEL
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from AI-powered institutional memory to medical device compliance and agricultural platforms, indicates a broad interest and ability to apply AI solutions across different domains. Their involvement in hackathons and publishing a conference paper suggests a proactive and collaborative attitude. The focus on agentic AI and full-stack development aligns well with the target role of an AI Engineer, particularly in an innovative environment.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through complex project implementations and an ownership-driven mindset. Their experience in team leadership and rapid prototyping suggests good operational fit for agile environments. The ability to work with AI-first development workflows indicates adaptability and a forward-thinking approach.