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I am an AI/ML Engineer with 6+ years of experience designing, developing, and deploying AI solutions across healthcare, financial services, and public sector domains, with a focus on responsible AI implementation and data security. I have hands-on experience with Amazon Bedrock and related AWS services (SageMaker, Lambda, ECS, S3, Step Functions) to build secure, scalable AI applications. I specialize in Retrieval-Augmented Generation (RAG) and semantic search to deliver grounded, explainable AI for enterprise use cases.\n\nI build AI-powered interfaces using React and TypeScript, design intuitive UI/UX for conversational AI and data-driven web apps, and lead end-to-end AI engineering initiatives—from data ingestion and model training to deployment, MLOps, and governance. I am proficient in Python and API development (FastAPI/Flask), and I routinely work with LangChain, FAISS, Pinecone, and foundation models to deliver robust, auditable AI capabilities while ensuring regulatory and security compliance.…I am an AI/ML Engineer with 6+ years of experience designing, developing, and deploying AI solutions across healthcare, financial services, and public sector domains, with a focus on responsible AI implementation and data security. I have hands-on experience with Amazon Bedrock and related AWS services (SageMaker, Lambda, ECS, S3, Step Functions) to build secure, scalable AI applications. I specialize in Retrieval-Augmented Generation (RAG) and semantic search to deliver grounded, explainable AI for enterprise use cases.\n\nI build AI-powered interfaces using React and TypeScript, design intuitive UI/UX for conversational AI and data-driven web apps, and lead end-to-end AI engineering initiatives—from data ingestion and model training to deployment, MLOps, and governance. I am proficient in Python and API development (FastAPI/Flask), and I routinely work with LangChain, FAISS, Pinecone, and foundation models to deliver robust, auditable AI capabilities while ensuring regulatory and security compliance.

Rajashekar Gangasani

AI Engineer, Data Scientist, Cloud Developer, +4

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I am an AI/ML Engineer with 6+ years of experience designing, developing, and deploying AI solutions across healthcare, financial services, and public sector domains, with a focus on responsible AI implementation and data security. I have hands-on experience with Amazon Bedrock and related AWS services (SageMaker, Lambda, ECS, S3, Step Functions) to build secure, scalable AI applications. I specialize in Retrieval-Augmented Generation (RAG) and semantic search to deliver grounded, explainable AI for enterprise use cases.\n\nI build AI-powered interfaces using React and TypeScript, design intuitive UI/UX for conversational AI and data-driven web apps, and lead end-to-end AI engineering initiatives—from data ingestion and model training to deployment, MLOps, and governance. I am proficient in Python and API development (FastAPI/Flask), and I routinely work with LangChain, FAISS, Pinecone, and foundation models to deliver robust, auditable AI capabilities while ensuring regulatory and security compliance.…I am an AI/ML Engineer with 6+ years of experience designing, developing, and deploying AI solutions across healthcare, financial services, and public sector domains, with a focus on responsible AI implementation and data security. I have hands-on experience with Amazon Bedrock and related AWS services (SageMaker, Lambda, ECS, S3, Step Functions) to build secure, scalable AI applications. I specialize in Retrieval-Augmented Generation (RAG) and semantic search to deliver grounded, explainable AI for enterprise use cases.\n\nI build AI-powered interfaces using React and TypeScript, design intuitive UI/UX for conversational AI and data-driven web apps, and lead end-to-end AI engineering initiatives—from data ingestion and model training to deployment, MLOps, and governance. I am proficient in Python and API development (FastAPI/Flask), and I routinely work with LangChain, FAISS, Pinecone, and foundation models to deliver robust, auditable AI capabilities while ensuring regulatory and security compliance.

Available to hire

I am an AI/ML Engineer with 6+ years of experience designing, developing, and deploying AI solutions across healthcare, financial services, and public sector domains, with a focus on responsible AI implementation and data security. I have hands-on experience with Amazon Bedrock and related AWS services (SageMaker, Lambda, ECS, S3, Step Functions) to build secure, scalable AI applications. I specialize in Retrieval-Augmented Generation (RAG) and semantic search to deliver grounded, explainable AI for enterprise use cases.\n\nI build AI-powered interfaces using React and TypeScript, design intuitive UI/UX for conversational AI and data-driven web apps, and lead end-to-end AI engineering initiatives—from data ingestion and model training to deployment, MLOps, and governance. I am proficient in Python and API development (FastAPI/Flask), and I routinely work with LangChain, FAISS, Pinecone, and foundation models to deliver robust, auditable AI capabilities while ensuring regulatory and security compliance.

Skills

Experience Level

Expert

Expert

Expert

Expert

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Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

English

Fluent

Work Experience

Gen AI ML Engineer at Citizens Financial Group

September 1, 2024 - Present

Led the design and deployment of a Generative AI-powered financial summarization and compliance system, delivering secure retrieval-augmented generation pipelines on GCP and AWS. Developed machine learning and deep learning models for credit risk analysis, time-series forecasting, and synthetic data generation. Integrated LLMs such as GPT-4, Gemini, Claude, and LLaMA2/3 with LangChain, FAISS, and Pinecone for advanced AI applications, automated audits, and financial chatbot interactions. Built scalable MLOps pipelines and containerized workflows using Docker, Kubernetes, Kubeflow, and Terraform, ensuring compliance with security standards and improving audit review efficiency by 50%.

Data Scientist/ ML Engineer at Charles Schwab

August 1, 2024 - August 26, 2025

Developed an LLM-powered advisory assistant leveraging RAG pipelines, fine-tuned models, and vector search to support financial analysts with natural language queries, streamlining investment reporting and compliance documentation. Led architecture and data mining initiatives for financial technology projects, built ETL pipelines on Azure, and deployed ML models using Azure ML, Databricks, and Spark. Utilized TensorFlow, PyTorch, and Spark MLlib for fraud detection and risk modeling, and containerized applications deployed via Azure DevOps and Jenkins. Designed scalable time-series forecasting models and implemented transformer-based NLP pipelines for real-time summarization.

Data Scientist at WellCare

December 1, 2022 - August 26, 2025

Developed ML and GenAI workflows for patient demand forecasting, claims analytics, and care planning using Spark, Kafka, Airflow, and AWS SageMaker. Built deep learning models analyzing health patterns and deployed AI-powered applications for automating care recommendations and healthcare operations. Scaled machine learning algorithms on large EHR and claims datasets, developed interactive AI applications, built real-time analytics dashboards, and automated CI/CD pipelines. Integrated SageMaker and Bedrock to experiment with hybrid models, improving patient scheduling and healthcare resource availability by 12%.

Cloud Engineer at Medpace

October 1, 2019 - August 26, 2025

Architected and maintained Azure hybrid cloud environments supporting scalable data and ML workloads. Designed and optimized ETL pipelines using Glue, Spark, Hadoop, and Data Factory, reducing latency and costs significantly. Migrated legacy jobs to Databricks, containerized workloads with Docker, orchestrated deployments on Kubernetes, and automated cloud provisioning with Terraform. Implemented monitoring with Prometheus and Azure Monitor and enforced GDPR-compliant security policies including RBAC, IAM, and encryption. Improved documentation reducing ramp-up time for new team members.

Gen AI Engineer at Cigna Healthcare

November 1, 2023 - Present

Led design and deployment of a RAG-powered clinical chatbot on Amazon Bedrock (Claude primary, LLaMA as failover). Built a document ingestion pipeline for guidelines and formulary PDFs, implemented hierarchical chunking, and generated Titan embeddings stored in OpenSearch with metadata filters. Implemented retrieval chains with LangChain and autonomous LangGraph agents, integrated Model Context Protocol (MCP) for secure access to databases and APIs, and applied Bedrock Guardrails for HIPAA-compliant, redacted responses. Created a React/TypeScript frontend and established automated evaluation with RAGAS and LangSmith. Set up CI/CD pipelines with Terraform, Docker, and ECS and produced AI governance artifacts for audit readiness.

Data Scientist/ ML Engineer at TCS (TNHSP)

October 1, 2021 - December 1, 2022

Developed and deployed ML models on Azure Cloud to predict patient readmission risk and automate clinical data processing for a government healthcare program. Built large-scale data ingestion and transformation pipelines in Azure Data Factory and PySpark, mapped EMR/lab/clinical data to FHIR schemas, engineered features, conducted EDA, and validated models with ROC-AUC and SHAP analyses. Deployed models as FastAPI microservices via Azure API Management and implemented MLflow, Airflow, and Azure DevOps CI/CD for full auditability. Provisioned Azure ML workspaces and Databricks clusters using Terraform/Bicep and configured monitoring dashboards for latency and data freshness.

Jr. Data Scientist at Birla Soft

August 1, 2020 - October 1, 2021

Developed a product-recommendation engine and built a centralized data lake in S3 via Python and AWS Glue. Performed data collection/cleaning, feature engineering, and exploratory data analysis; trained models (logistic regression, random forest, XGBoost) and evaluated using precision/recall/F1. Implemented deployment of ML workflows with MLflow, Airflow, and CI/CD (Jenkins), and supported infrastructure as code with Terraform. Exposed FastAPI microservices for basic model inference and integrated with existing platforms.