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AI Engineer with 2+ years in LLM-powered applications & agentic AI workflows
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A student with hands-on experience building LLM-powered applications, RAG pipelines, and agentic AI workflows using OpenAI-compatible APIs, LangChain, and vector databases. Published IEEE researcher with a track record of delivering measurable performance gains (25-40%) across ML/DL projects. Proficient in Python, FastAPI, PostgreSQL, Docker, and Streamlit. Actively building with Claude, NVIDIA NIM (Llama 3.1), and multi-agent architectures - and seeking to grow further as part of a fast-moving Gen AI team.
Lingaya's Vidyapeeth
B.Tech · Computer Science & Engineering (Data Science)
August 1, 2022 – June 30, 2026
NIELIT – HQ
AI & ML – WBL Intern
July 1, 2025 – June 1, 2026
India
NASSCOM
Gen AI Engineer Intern
January 1, 2025 – December 1, 2025
India
Self-Healing Data Pipeline
June 18, 2026 – Present
Designed an autonomous pipeline that ingests raw CSV/API/DB data, validates it against configurable rules, detects statistical anomalies, and calls an LLM to diagnose issues and generate Python fix-code on the fly — zero manual intervention. Implemented a re-validate-and-retry loop (max 3x) with quarantine fallback; LLM-generated fixes execute against a sandboxed DataFrame copy with restricted namespace for safety. Structured repair audit log (SQLite) capturing diagnosis, confidence score, fix code, and outcome — enabling full traceability and human review for low-confidence repairs. Tech mirrors the VanyaIT stack: OpenAI-compatible SDK (NVIDIA), REST-based LLM calls, PostgreSQL storage, and modular FastAPI-ready microservice design.
View ProjectAI SQL Assistant
June 18, 2026 – Present
Built a natural language → SQL query tool using a LangChain SQL Agent backed by NVIDIA NIM's fine-tuned Text-to-SQL model (Llama 3.1 Nemotron); schema-aware prompting injects live DDL context into every request. Streamlit UI with query-display toggle deployed over PostgreSQL; supports complex multi-join queries by swapping model endpoint without code changes. Directly demonstrates RAG-adjacent retrieval (schema injection), prompt engineering, and REST API orchestration — core skills for the VanyaIT role.
View ProjectCoreMaintainer ID – GitHub Analysis Tool
June 18, 2026 – Present
Full-stack Streamlit dashboard fetching and visualizing repository network metrics, PR trends, and health scores across 1,200+ data points; improved contribution efficiency by 40%.
View ProjectIntroduction to Digital Twins
LinkedIn Learning
December 1, 2025 – Present
Cybersecurity Fundamentals
IBM SkillsBuild
December 1, 2025 – Present
Blockchain: Beyond the Basics
LinkedIn Learning
December 1, 2025 – Present
Artificial Intelligence Analyst
IBM Career Education Program
July 1, 2025 – Present
Overview of Space Science & Technology
ISRO START Programme
September 1, 2023 – Present
Cultural Fit Analysis
The candidate's project diversity, including an AI SQL Assistant, a Self-Healing Data Pipeline, and a GitHub Analysis Tool, demonstrates a broad interest and capability in applying AI across different domains. The experience with various LLM providers (NVIDIA NIM, Claude API, OpenAI API) and frameworks (LangChain, LangGraph) indicates adaptability and a willingness to explore different technologies. The involvement in research and publications (IEEE Journal) suggests a proactive and inquisitive nature, which aligns well with a fast-moving Gen AI team. The listed certifications, while varied, show a commitment to continuous learning. The target role of 'AI Engineer' aligns well with the candidate's demonstrated skills and project focus.
Soft Skills & Operational Fit
The candidate's project descriptions highlight an ability to design autonomous systems, implement re-validation/retry loops, and structure audit logs, indicating a detail-oriented and robust approach to problem-solving. The focus on measurable improvements (e.g., 40% improved contribution efficiency, 28% cut downtime) suggests a results-driven mindset. The experience with multi-agent systems and prompt engineering points to an understanding of complex AI system design and optimization. However, without specific psychometric or English test results, a comprehensive assessment of soft skills like logical reasoning, work attitude, stress handling, and team collaboration is not possible.