Raghavendra Kare

Designed and deployed a Generative AI-powered financial document intelligence platform, leveraging fine-tuned LLMs (GPT, LLaMA) to automate credit risk analysis, fraud detection, and compliance checks. Implemented Retrieval-Augmented Generation (RAG) pipelines with FAISS, Pinecone, and ElasticSearch for domain-specific knowledge retrieval, improving accuracy of financial reporting and document summarization. Built end-to-end ML pipelines on AWS SageMaker and Glue for ingesting, cleaning, and analyzing structured/unstructured banking datasets. Fine-tuned domain-specific LLMs using LoRA, PEFT, and instruction tuning for regulatory reporting, KYC/AML verification, and internal knowledge search. Implemented explainable AI frameworks; ensured compliance. Optimized LLM inference with AWS Inferentia and SageMaker Neo for low-latency apps. Developed ETL and real-time data pipelines using Apache Spark, Glue, and Kafka. Applied data augmentation to improve model robustness. Established MLOps pra

Designed and implemented Generative AI–based healthcare virtual assistant (GPT-3/transformers) to automate claims inquiries, benefit explanations, and provider interactions. Built Retrieval-Augmented Generation (RAG) pipelines with FAISS and Elasticsearch for knowledge retrieval from policy documents, clinical guidelines, and member communication archives. Built NLP/NLU models for intent detection, entity recognition, and contextual response generation. Automated summarization workflows for clinical notes, authorization forms, and case management reports. Implemented speech-to-text and text-to-speech to support multilingual member engagement. Built end-to-end ML pipelines on Google Cloud Vertex AI and BigQuery; automated data ingestion with Airflow and Spark. Implemented MLOps with MLflow; ensured HIPAA compliance; integrated OpenAI GPT APIs with internal systems; developed predictive analytics models for churn, care gaps, and plan optimization. Collaborated with clinical teams to en

Joined Centene as a Senior AI / ML Engineer, leading AI-driven solutions focused on healthcare claims intelligence and member support automation. Built Conversational AI agents using Google Vertex AI and Dialogflow CX to support member and agent interactions, with HIPAA-compliant data handling. Implemented NLP/NLU capabilities including intent classification, slot filling, and context management for complex healthcare conversations. Developed retrieval-augmented QA workflows using FAISS and Elasticsearch to fetch coverage rules and policy clauses. Tuned embeddings and applied PEFT / prompt tuning to improve domain relevance while controlling cost. Built data/ML pipelines on Google Vertex AI, supporting batch training, model versioning, and controlled deployment. Exposed AI outputs via REST APIs and integrated with member portals, enabling human-in-the-loop reviews. Evaluated model quality with precision, recall, F1, and clinically aligned validation. Collaborated with clinical teams, c