I’m a Senior AI Engineer at The Hartford, specializing in Generative AI, Agentic AI, RAG, and MCP-based automation. Currently, I’m working on a Meta Context Engineering Repository and an AI-enabled connected feature-delivery workflow that streamlines the software development lifecycle from Rally user stories to non-production deployment. I’m particularly good at building LLM-powered applications, designing RAG pipelines, integrating tools through MCP, and developing scalable Python-based AI solutions using FastAPI, LangChain, and LangGraph.
Data Science Intern
Genpact
- • Completed Data Science training (94%) and Generative AI training (90%) with distinction.
- • Built Python NLP/LLM prototypes that improved search accuracy by 30% and reduced search time by 25%.
- • Assisted with Databricks ETL development for downstream ML and GenAI workflows
AI Engineer
Genpact
- Architected and deployed production-grade Computer Vision and GenAI platforms using Python, FastAPI, CNN-based models,
- LangChain, embeddings, and vector databases for enterprise AI use cases.
- • Developed ML models and feature pipelines for large-scale datasets, improving model performance and reducing processing time by
- 35%.
- • Designed end-to-end Computer Vision pipelines covering preprocessing, feature extraction, inference, and integration with
- LLM-powered RAG systems for document and image understanding.
- • Engineered single- and dual-RAG architectures, improving retrieval precision by 32% and reducing response latency by 40% in
- enterprise chatbot and report-generation applications.
- • Decomposed monolithic AI workflows into modular services for CV inference, RAG, LLMs, ingestion, and orchestration, reducing
- execution time by 64%.
- • Implemented parallel retrieval and ingestion strategies and deployed cloud-native AI services using AWS Lambda, S3, EC2, IAM,
- Docker, Jenkins, and GitHub Actions.
- • Recognized as a Top Performer (Cheer Points) for GenAI and Computer Vision contributions; delivered projects 20% ahead of
- schedule.
SENIOR AI ENGINEER
THE HARTFORD
- Building the HIG AI Portfolio Kit, a governed application-context layer across five dimensions: System, Delivery, Change, Business Intent, and Governance.
- Developing an MCP-enabled, nine-stage feature-delivery workflow from Rally story to non-production deployment.
- Integrating Knowledge RAG, GitHub, and Rally MCPs to unify retrieval, repo actions, and story context.
- Applying governed-AI controls: read-only source capture, evidence provenance, bounded retrieval, and human review.
AI-Powered Document Deduplication and Consolidation
Built an AI-powered document processing system that identifies duplicate documents, clusters similar content, consolidates information, and generates summaries using NLP and LLMs.
Real-Time Customer Engagement Banking Platform
Developed a real-time customer engagement platform leveraging machine learning, CNNs, and NLP to support personalized banking interactions and improve customer experience.
DigiHarvest – Data Science Web Application
Developed a data science web application focused on helping farmers improve crop production using data-driven insights and machine learning techniques.
Steganography-Based Chat Application
Developed a Java-based chat application exploring image steganography to conceal messages within image files, combining communication with an approach to discreet message exchange.
Full-Stack MERN CRUD Application
Developed a full-stack web application demonstrating CRUD (Create, Read, Update, Delete) operations, with a JavaScript-based frontend and a MERN-stack project structure.
B.Tech Computer Science and Engineering
Sastra Deemed University · 9.34
Google Generative AI Leader
Google Cloud Digital Leader
Databricks Generative AI Certification
Databricks
Sept 2025AWS Certified AI Practitioner
AWS
Microsoft Certified: Azure AI Fundamentals
Microsoft
Attendee · Oct 24, 2026