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AI Engineer with 1+ years 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 – June 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 AI & Machine Learning
Harvard CS50
June 1, 2026 – Present
Data Science
Infosys
June 1, 2026 – Present
Introduction to Databases with SQL
Harvard CS50
June 1, 2026 – Present
Machine Learning I
Columbia
June 1, 2026 – Present
Introduction to Programming with Python
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 projects showcase a strong interest in applying AI to real-world problems, including regulated industries and agricultural access, indicating a diverse problem-solving approach. Their academic background with an AI specialization and numerous certifications aligns well with a continuous learning culture. However, the lack of professional experience and focus solely on academic projects might indicate a need for mentorship in navigating corporate environments and large-scale team collaboration beyond project-based work.
Soft Skills & Operational Fit
The candidate demonstrates strong ownership, adaptability, and a rapid prototyping mindset through their project descriptions and technical achievements. Their experience in regulated industries (healthcare/life-sciences) suggests an understanding of robust system design and compliance, which is valuable for operational fit. The ability to work independently on full-stack applications without prior internship experience highlights self-sufficiency and initiative.