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Hi, I’m Bhargavi T, a Senior Generative AI & LLM Engineer with 6+ years of hands-on experience designing enterprise-grade RAG systems, LLM-powered assistants, and agentic AI workflows. I specialize in LLM orchestration, retrieval pipelines, semantic search, embeddings, tool-use agents, and multi-agent reasoning to automate insights, decision support, and end-to-end workflow automation. I’ve built scalable MLOps platforms and GenAI solutions across finance and healthcare, delivering 3–5x faster knowledge retrieval and 35–45% reductions in documentation effort, while ensuring secure, governed deployments aligned with HIPAA/GDPR and AI safety standards. I’m passionate about collaborating with product, engineering, and compliance teams to drive enterprise GenAI adoption and measurable ROI. My focus lies in secure LLM deployment, governance, evaluation metrics, drift detection, and automated retraining, all powered by modern tools like OpenAI/Azure OpenAI, LangChain/LangGraph, Semantic Kernel, and vector databases, across Azure, AWS, and GCP.…Hi, I’m Bhargavi T, a Senior Generative AI & LLM Engineer with 6+ years of hands-on experience designing enterprise-grade RAG systems, LLM-powered assistants, and agentic AI workflows. I specialize in LLM orchestration, retrieval pipelines, semantic search, embeddings, tool-use agents, and multi-agent reasoning to automate insights, decision support, and end-to-end workflow automation. I’ve built scalable MLOps platforms and GenAI solutions across finance and healthcare, delivering 3–5x faster knowledge retrieval and 35–45% reductions in documentation effort, while ensuring secure, governed deployments aligned with HIPAA/GDPR and AI safety standards. I’m passionate about collaborating with product, engineering, and compliance teams to drive enterprise GenAI adoption and measurable ROI. My focus lies in secure LLM deployment, governance, evaluation metrics, drift detection, and automated retraining, all powered by modern tools like OpenAI/Azure OpenAI, LangChain/LangGraph, Semantic Kernel, and vector databases, across Azure, AWS, and GCP.

Bhargavi T

AI Engineer, Data Scientist, Data Analyst, +2

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Hi, I’m Bhargavi T, a Senior Generative AI & LLM Engineer with 6+ years of hands-on experience designing enterprise-grade RAG systems, LLM-powered assistants, and agentic AI workflows. I specialize in LLM orchestration, retrieval pipelines, semantic search, embeddings, tool-use agents, and multi-agent reasoning to automate insights, decision support, and end-to-end workflow automation. I’ve built scalable MLOps platforms and GenAI solutions across finance and healthcare, delivering 3–5x faster knowledge retrieval and 35–45% reductions in documentation effort, while ensuring secure, governed deployments aligned with HIPAA/GDPR and AI safety standards. I’m passionate about collaborating with product, engineering, and compliance teams to drive enterprise GenAI adoption and measurable ROI. My focus lies in secure LLM deployment, governance, evaluation metrics, drift detection, and automated retraining, all powered by modern tools like OpenAI/Azure OpenAI, LangChain/LangGraph, Semantic Kernel, and vector databases, across Azure, AWS, and GCP.…Hi, I’m Bhargavi T, a Senior Generative AI & LLM Engineer with 6+ years of hands-on experience designing enterprise-grade RAG systems, LLM-powered assistants, and agentic AI workflows. I specialize in LLM orchestration, retrieval pipelines, semantic search, embeddings, tool-use agents, and multi-agent reasoning to automate insights, decision support, and end-to-end workflow automation. I’ve built scalable MLOps platforms and GenAI solutions across finance and healthcare, delivering 3–5x faster knowledge retrieval and 35–45% reductions in documentation effort, while ensuring secure, governed deployments aligned with HIPAA/GDPR and AI safety standards. I’m passionate about collaborating with product, engineering, and compliance teams to drive enterprise GenAI adoption and measurable ROI. My focus lies in secure LLM deployment, governance, evaluation metrics, drift detection, and automated retraining, all powered by modern tools like OpenAI/Azure OpenAI, LangChain/LangGraph, Semantic Kernel, and vector databases, across Azure, AWS, and GCP.

Available to hire

Hi, I’m Bhargavi T, a Senior Generative AI & LLM Engineer with 6+ years of hands-on experience designing enterprise-grade RAG systems, LLM-powered assistants, and agentic AI workflows. I specialize in LLM orchestration, retrieval pipelines, semantic search, embeddings, tool-use agents, and multi-agent reasoning to automate insights, decision support, and end-to-end workflow automation. I’ve built scalable MLOps platforms and GenAI solutions across finance and healthcare, delivering 3–5x faster knowledge retrieval and 35–45% reductions in documentation effort, while ensuring secure, governed deployments aligned with HIPAA/GDPR and AI safety standards.

I’m passionate about collaborating with product, engineering, and compliance teams to drive enterprise GenAI adoption and measurable ROI. My focus lies in secure LLM deployment, governance, evaluation metrics, drift detection, and automated retraining, all powered by modern tools like OpenAI/Azure OpenAI, LangChain/LangGraph, Semantic Kernel, and vector databases, across Azure, AWS, and GCP.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Work Experience

Senior AI/ML Engineer at Northern Trust

April 1, 2025 - Present

Architected a unified multi-cloud GenAI platform using Python, OpenAI/Azure OpenAI, AWS Lambda, and GCP Vertex AI to standardize LLM deployment, evaluation, and governance, reducing AI rollout time by 40% across business units. Designed and deployed enterprise RAG pipelines with LangChain, FAISS, GPT-4/Gemini, and custom embeddings, delivering ~3x faster knowledge retrieval. Built agentic AI assistants with LangGraph and Semantic Kernel to automate complex analytical tasks in finance, operations, and risk. Developed LLM-powered summarization and Q&A systems to cut manual analyst effort by ~45% and accelerate reporting. Implemented drift-aware retraining pipelines (MLflow, ADF, Lambda) for automated model refresh and drift alerts. Deployed secure GenAI & RAG APIs (FastAPI, Kubernetes, Azure Functions) with RBAC, VNET isolation, TLS, Key Vault, and audit logging. Reduced LLM retrieval latency by 70% with advanced embeddings and Databricks/Spark optimizations. Collaborated with product, a

AI/ML Engineer | Generative AI & MLOps Engineer at Molina Healthcare

May 1, 2023 - March 31, 2025

Delivered clinical-grade GenAI systems improving documentation, triage, coding, and physician efficiency. Built HIPAA-compliant Clinical GenAI Assistant using Python, Azure OpenAI, LangChain, and RAG pipelines to automate FAQs, triage support, prescription workflows, and prior authorization queries—reducing provider support workload by ~40%. Productionized LLM-based summarization and clinical documentation automation, enabling structured SOAP notes and chart updates, cutting documentation time by 35–45%. Engineered RAG pipelines retrieving context from EHRs, labs, radiology reports, guidelines, claims, and patient histories (FAISS/Pinecone) for 5x faster access to clinical facts. Fine-tuned GPT-4, LLaMA2, BERT, and T5 for medical NER, ICD/CPT code suggestion, clinical Q&A, and risk classification. Developed real-time clinical decision support APIs (FastAPI + Kubernetes + MLflow). Implemented LLM evaluation, safety controls, and drift-aware retraining. Integrated OCR (OpenCV/Tessera

Data Scientist | Cloud Data Pipelines | Spark & AWS | Early MLOps Contributor at Wipro India

July 1, 2019 - July 1, 2022

Built AI-ready data platforms and automated pipelines supporting ML and analytics across large enterprises. Designed and optimized Python + PySpark pipelines in Databricks to create ML-ready feature datasets. Developed metadata-driven ingestion frameworks using AWS Glue, Lambda, and Step Functions, automating hundreds of batch workflows and reducing orchestration overhead by ~80%. Enhanced ML preprocessing performance via Spark optimizations. Created high-quality feature engineering modules and data quality validation layers. Implemented CI/CD pipelines for ML data flows and built SLA dashboards (CloudWatch & QuickSight) to monitor latency and throughput. Collaborated to define data contracts and feature consumption patterns, contributing to Lakehouse medallion architecture.

Intern Software Engineer | Data Engineering & Cloud Migration at APTOnline India

January 1, 2019 - June 1, 2019

Contributed to cloud migration and AI-ready data preparation pipelines for analytics and ML pilots. Assisted in migrating on-prem datasets to Azure Data Lake and AWS S3, enabling scalable storage for analytics/ML teams. Developed Python-based ETL scripts for data cleaning, validation, and transformation. Supported creation of ADF and AWS Glue ingestion pipelines, automating batch workflows. Implemented basic feature preprocessing routines and optimized early Spark jobs. Built lightweight automation utilities to assist engineering teams with repetitive tasks.