user1809268
I’m a senior AI/ML engineer with 10+ years of experience building production-grade machine learning, LLM, and generative AI solutions across retail/e-commerce, healthcare, and infrastructure environments.
About
I’m a senior AI/ML engineer with 10+ years of experience building production-grade machine learning, LLM, and generative AI solutions across retail/e-commerce, healthcare, and infrastructure environments. I specialize in LLM apps, RAG, agentic AI, NLP, computer vision, recommendation and forecasting systems, and the MLOps practices needed to move models from experimentation into scalable, reliable deployments.
In my recent roles, I’ve designed end-to-end systems such as RAG-based conversational product discovery, personalized recommendations using behavioral and search signals, and inventory/demand forecasting pipelines. I also focus heavily on evaluation and observability—measuring retrieval relevance, response quality, hallucination risk, latency, and token usage—while implementing CI/CD-driven MLOps workflows with monitoring, automated testing, and controlled rollouts.
- 9 years' experience
- Joined September 2026
Industry experience
- Healthcare
- Retail
- Software & Internet
- Professional Services
- Computers & Electronics
- Government
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Work experience· 6 roles
Built and designed GenAI-powered conversational product discovery capabilities for Home Depot’s digital commerce experience using LangChain, LLM APIs, RAG, embeddings, and vector search to map natural-language requests to relevant home-improvement products. Developed RAG pipelines over structured catalog data, including specifications, documentation, FAQs, and knowledge content using FAISS/Pinocchio-style indexing to improve retrieval quality for complex product and project questions. Implemented recommendation/ranking capabilities using customer interactions, product and search signals, improving personalization across shopping journeys. Engineered NLP-based product compatibility and attribute-matching workflows (embeddings, classification, semantic retrieval) and built forecasting + inventory intelligence models using Python, PyTorch, Spark, and AWS with MLOps practices. Developed GenAI automation workflows for product-content enrichment (attribute normalization, categorization, su
Designed and developed GenAI-powered conversational product discovery capabilities for Home Depot’s digital commerce environment using LangChain, LLM APIs, RAG, embeddings, and vector search to help customers identify relevant home-improvement products from natural-language requirements. Built RAG pipelines over structured product catalog data, specifications, product documentation, FAQs, and knowledge content using FAISS/Pinecone to improve retrieval quality for complex product and project-related questions. Developed AI-driven recommendation and ranking using customer interaction, product, search, and behavioral signals to improve personalization and product discovery across digital shopping journeys. Engineered product compatibility and attribute-matching workflows using NLP, embeddings, classification, and semantic retrieval. Developed demand forecasting and inventory intelligence models with Python, PyTorch, Spark, and AWS to support inventory planning and product availability a
Designed an AI-driven risk surveillance platform processing 200+ CCTV feeds in real time using GCP Cloud Run, Pub/Sub, and Cloud Functions. Fine-tuned LLMs for chatbot-style recommendations and built synthetic datasets using LangChain and OpenAI APIs for domain adaptation.
Education
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