Gen AI Engineer at Molina HealthCare

Led the design and deployment of multi-agent GenAI systems (LangChain, LangGraph, Semantic Kernel) enabling autonomous task execution, reasoning, and workflow integration within enterprise platforms. Built production-grade GenAI copilots for document classification, product intelligence, pricing analysis, and operational decisions. Implemented grounded Retrieval-Augmented Generation (RAG) pipelines with FAISS, ChromaDB, Pinecone, Weaviate, and Azure Cognitive Search, delivering accurate, grounded responses. Leveraged foundation models (GPT-4/4o, Claude 3, Gemini 1.5, LLaMA 3) for summarization, semantic search, and role-based content generation. Created dynamic agent orchestration graphs with tool-use, memory persistence, and self-healing flows. Developed LLM evaluation/observability using LangSmith, TruLens, PromptLayer, Prometheus, Grafana to monitor hallucination, grounding, latency, and cost. Integrated vector memory (Redis, embeddings) for context retention. Implemented safety/gov

AI/ ML Engineer at Petco

Designed and implemented autonomous LLM agents using LangChain, LangGraph, and Semantic Kernel to support merchandising analysis, product discovery, and customer engagement workflows, enabling multi-step reasoning and tool usage. Built production-grade Retrieval-Augmented Generation (RAG) frameworks using FAISS, Weaviate, Pinecone, and Azure Cognitive Search to deliver grounded insights from product catalogs, supplier data, customer reviews, and inventory documentation. Fine-tuned and optimized foundation models (GPT-4/4o, Claude, LLaMA, Mistral) using LoRA and supervised fine-tuning to generate retail-specific insights, product summaries, and personalized recommendations. Engineered agent orchestration pipelines chaining retrieval, reasoning, summarization, and validation steps to automate merchandising insights, assortment planning support, and executive reporting. Designed and implemented event-driven architectures using AWS Event Bridge, Kafka, and RabbitMQ to support scalable, asy

Data Scientist at Bank of America

Designed data models and analytical datasets to support revenue forecasting, client analytics, and business performance prediction. Collaborated with data engineering on data extraction, normalization, and transformation; aligned with Data Governance for data quality and standards. Applied time-series forecasting, PCA, and predictive modeling; evaluated ML approaches to improve forecasting accuracy. Built dashboards and reports to communicate insights; supported strategic planning and financial decision-making, ensuring data accuracy and reliability across enterprise outputs.

Python Full Stack Developer at Pure Storage

Led end-to-end SDLC for backend and frontend components. Designed and implemented database models, REST APIs, and backend services using Python (Django/Flask) with PostgreSQL/MySQL. Migrated legacy Java/Python scripts to optimized Python modules; built microservices in Python, Go, and Rust. Implemented event-driven processing with Kafka, Celery, RabbitMQ, and Redis; deployed cloud-native solutions on AWS/GCP/Azure with CI/CD automation. Built and maintained frontend components, containerized services with Docker, and orchestrated with Kubernetes. Applied performance tuning and data processing using PySpark and related tools.

Python Developer at Value Labs - Hyderabad

Developed reusable Python modules and backend services using Django/REST. Implemented event-driven architectures with AWS-based and messaging systems (Kafka, RabbitMQ). Integrated enterprise data sources for real-time analytics and reporting; designed cost-aware prompt routing and model selection logic; managed memory for AI agents; built GenAI microservices and pipelines with FastAPI, Docker, and Kubernetes; delivered dashboards for merchandising and analytics.