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AI Engineer with 1+ years in Agentic AI, RAG & FastAPI
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI/ML Engineer with experience building Agentic AI, Generative AI, Retrieval-Augmented Generation (RAG), semantic retrieval systems, and FastAPI-based backend services. Skilled in multi-agent workflows, vector databases, prompt engineering, ETL/ELT concepts, enterprise AI automation, and production-ready AI applications using Python, LangChain, FAISS, ChromaDB, and Pinecone.
Siksha 'O' Anusandhan University
B.Tech · Computer Science & Engineering
August 1, 2020 – June 30, 2024
TATA Consultancy Services Ltd
AI Engineer
October 2, 2024 – Present
Gurgaon, Haryana, India
Soven Developer
Frontend Developer Intern
June 1, 2024 – September 1, 2024
India
Enterprise AI SQL Copilot
November 1, 2024 – Present
Built an enterprise Text-to-SQL AI Copilot using RAG, semantic retrieval, and schema-aware SQL generation. Designed a multi-agent architecture with Intent Detection, Schema Linking, Query Planning, Validation, and Confidence Scoring agents. Implemented document ingestion, chunking, embedding generation, indexing, semantic retrieval, and vector search workflows using FAISS. Built data transformation, validation, and retrieval evaluation pipelines to improve response quality, explainability, and AI reliability. Evaluated FAISS, ChromaDB, and Pinecone-based retrieval approaches to optimize semantic search performance and contextual relevance.
Enterprise AI Conversational Platform
November 1, 2024 – Present
Built enterprise conversational AI solutions using LangChain, RAG pipelines, semantic retrieval, and knowledge-grounded response generation. Implemented FAISS-based retrieval systems enabling contextual document search and enterprise knowledge access. Developed custom Python SDKs and integrated enterprise systems into MCP-based orchestration workflows for end-to-end automation. Designed retrieval, validation, and workflow orchestration mechanisms to improve contextual relevance, response consistency, and AI reliability.
AWS Certified AI Practitioner
Amazon Web Services
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects are highly aligned with the target role of an AI Engineer, demonstrating a clear focus on enterprise AI solutions. The breadth of skills covers various aspects of AI development, from core AI/ML to data engineering and backend services. However, the experience is limited to AI-specific projects, and there is no explicit mention of open-source contributions, community involvement, or diverse team collaboration experiences beyond project work, which could indicate a narrower cultural fit in terms of broader organizational engagement.
Soft Skills & Operational Fit
The candidate's project descriptions highlight collaboration and a focus on improving usability and performance, indicating a team-oriented and results-driven approach. The emphasis on 'explainability' and 'AI reliability' in projects suggests a strong operational fit for robust AI system development.