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Hello, I’m Thabitha Kaveti, an AI/ML & GenAI Engineer with 10+ years of delivering enterprise-scale AI solutions across healthcare, finance, automotive, and large-scale enterprise platforms. I specialize in Generative AI, LLMs, RAG pipelines, and end-to-end MLOps, turning complex data into actionable insights that reduce fraud, improve outcomes, and ensure regulatory compliance. I thrive in highly regulated environments and enjoy translating AI capabilities into strategic business value for executives, compliance teams, and product stakeholders. I am passionate about advancing GenAI adoption responsibly, collaborating with cross-functional teams, and continuously learning cutting-edge tools (Agentic AI workflows, vector databases, semantic search) to build secure, scalable AI platforms across multi-cloud ecosystems.…Hello, I’m Thabitha Kaveti, an AI/ML & GenAI Engineer with 10+ years of delivering enterprise-scale AI solutions across healthcare, finance, automotive, and large-scale enterprise platforms. I specialize in Generative AI, LLMs, RAG pipelines, and end-to-end MLOps, turning complex data into actionable insights that reduce fraud, improve outcomes, and ensure regulatory compliance. I thrive in highly regulated environments and enjoy translating AI capabilities into strategic business value for executives, compliance teams, and product stakeholders. I am passionate about advancing GenAI adoption responsibly, collaborating with cross-functional teams, and continuously learning cutting-edge tools (Agentic AI workflows, vector databases, semantic search) to build secure, scalable AI platforms across multi-cloud ecosystems.

Thabitha Kaveti

AI Engineer, Data Scientist, Developer, +6

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Hello, I’m Thabitha Kaveti, an AI/ML & GenAI Engineer with 10+ years of delivering enterprise-scale AI solutions across healthcare, finance, automotive, and large-scale enterprise platforms. I specialize in Generative AI, LLMs, RAG pipelines, and end-to-end MLOps, turning complex data into actionable insights that reduce fraud, improve outcomes, and ensure regulatory compliance. I thrive in highly regulated environments and enjoy translating AI capabilities into strategic business value for executives, compliance teams, and product stakeholders. I am passionate about advancing GenAI adoption responsibly, collaborating with cross-functional teams, and continuously learning cutting-edge tools (Agentic AI workflows, vector databases, semantic search) to build secure, scalable AI platforms across multi-cloud ecosystems.…Hello, I’m Thabitha Kaveti, an AI/ML & GenAI Engineer with 10+ years of delivering enterprise-scale AI solutions across healthcare, finance, automotive, and large-scale enterprise platforms. I specialize in Generative AI, LLMs, RAG pipelines, and end-to-end MLOps, turning complex data into actionable insights that reduce fraud, improve outcomes, and ensure regulatory compliance. I thrive in highly regulated environments and enjoy translating AI capabilities into strategic business value for executives, compliance teams, and product stakeholders. I am passionate about advancing GenAI adoption responsibly, collaborating with cross-functional teams, and continuously learning cutting-edge tools (Agentic AI workflows, vector databases, semantic search) to build secure, scalable AI platforms across multi-cloud ecosystems.

Available to hire

Hello, I’m Thabitha Kaveti, an AI/ML & GenAI Engineer with 10+ years of delivering enterprise-scale AI solutions across healthcare, finance, automotive, and large-scale enterprise platforms. I specialize in Generative AI, LLMs, RAG pipelines, and end-to-end MLOps, turning complex data into actionable insights that reduce fraud, improve outcomes, and ensure regulatory compliance. I thrive in highly regulated environments and enjoy translating AI capabilities into strategic business value for executives, compliance teams, and product stakeholders.

I am passionate about advancing GenAI adoption responsibly, collaborating with cross-functional teams, and continuously learning cutting-edge tools (Agentic AI workflows, vector databases, semantic search) to build secure, scalable AI platforms across multi-cloud ecosystems.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Work Experience

Gen AI Engineer at Change Healthcare

May 1, 2024 - Present

Designed and deployed GenAI-powered claims automation pipelines on AWS SageMaker and AWS Bedrock, improving accuracy and reducing manual review time in large-scale healthcare transactions. Fine-tuned GPT, BERT, and Hugging Face Transformers for entity recognition, anomaly detection, and contextual knowledge retrieval. Built retrieval-augmented generation (RAG) pipelines with LangChain, FAISS, and LangGraph. Automated ML pipelines with Airflow, MLflow, and Kubeflow for reproducibility and monitoring. Deployed fraud detection models for real-time scoring, engineered big data pipelines on Apache Spark, and delivered scalable RESTful APIs and dashboards for leadership to monitor fraud risks and claims analytics. Ensured HIPAA-secure deployments with encryption and audit logging and applied SHAP/LIME explainability.

Gen AI Engineer/Data Scientist at Broadridge Financials

April 1, 2024 - October 2, 2025

Developed an enterprise-grade advisory system using LLMs and predictive models to generate personalized investment insights. Achieved 29% improvement in recommendation accuracy and 17% gains in portfolio returns. Built supervised ML models (Scikit-learn, XGBoost) to predict investment preferences and risk tolerance; deployed deep learning architectures (LSTMs, Transformers) for market sentiment and behavior analysis; fine-tuned LLMs on analyst reports and news feeds. Engineered large-scale data pipelines (Azure Databricks) and automated ML pipelines (Airflow, MLflow, Kubeflow). Delivered real-time APIs (FastAPI/Flask) and secure deployment on AKS with Azure ML Studio; provided SHAP/LIME explanations for transparency; integrated vector search (FAISS, Pinecone) for semantic research.

AI/ML Engineer at Tesla

January 1, 2022 - October 2, 2025

Built churn models using supervised and unsupervised learning to identify high-risk customers and create granular segments for retention campaigns. Developed NLP pipelines to extract churn signals from support tickets and feedback; implemented end-to-end data pipelines on GCP; established automated retraining and monitoring (Airflow, Kubeflow) and deployed real-time scoring APIs (FastAPI/Flask) on GKE. Achieved 18% improvements in recall and precision; delivered real-time dashboards for executives and reduced churn by 24% with data-driven interventions.

Data Scientist at Oracle

May 1, 2020 - October 2, 2025

Developed AI-driven demand forecasting and classification models for manufacturing and retail, combining ARIMA/Prophet/LSTMs for time-series forecasts; built scalable ETL pipelines with Spark/Hadoop and Pandas/Numpy; implemented anomaly detection and supplier segmentation; deployed models via Flask/FastAPI on AWS with RBAC and encryption; delivered end-to-end supply chain forecasting that reduced holding costs by $2.1M annually and improved service reliability.

Data Analyst at Solugenix

November 1, 2017 - October 2, 2025

Designed and optimized ETL pipelines, boosted forecast accuracy by 22%, and increased campaign ROI by 15% through predictive analytics. Built real-time dashboards in Power BI and Tableau, performed segmentation with Scikit-learn, and implemented secure pipelines in Azure Data Lake; collaborated with sales and marketing to align KPIs and reporting with business goals.

Gen AI Engineer at Change Healthcare

May 1, 2024 - November 11, 2025

Designed and deployed AI/ML pipelines on AWS SageMaker and Bedrock to automate healthcare claims validation, detect anomalies, and strengthen HIPAA-compliant workflows for large-scale claims processing. Built and optimized end-to-end ML pipelines (scikit-learn, TensorFlow, PyTorch, XGBoost) and data processing to detect fraud, segment providers, and classify claims. Developed Retrieval-Augmented Generation (RAG) architectures with LangChain, FAISS, and Neo4j to support contextual knowledge retrieval for provider and claims data. Fine-tuned Transformer LLMs (GPT, BERT, RoBERTa) on healthcare domain text, improving entity extraction and summarization. Implemented automated ETL with AWS Glue, Athena, and S3; orchestrated pipelines with Airflow, MLflow, and SageMaker Pipelines; containerized services with Docker and Kubernetes; built real-time inference APIs with FastAPI. Applied Explainable AI with SHAP and LIME; deployed model monitoring and compliance safeguards; built executive dashboa

Gen AI Engineer/Data Scientist at Broadridge Financials

April 1, 2024 - April 1, 2024

Designed AI/ML-based financial analytics on Azure. Built predictive pipelines in Azure ML and Azure Databricks for churn, asset-risk, and portfolio optimization. Developed time-series forecasting (ARIMA, Prophet, LSTM) in Azure Synapse. Finetuned FinBERT via Azure OpenAI for financial text summarization and sentiment. Used PySpark within Azure Databricks for feature engineering; performed Bayesian inference/Monte Carlo for portfolio risk and diversification. Leveraged Neo4j and Azure Cosmos DB for graph analytics; built deep learning architectures (TensorFlow, PyTorch) for trade classification and anomaly detection. Automated MLOps using Azure ML Pipelines, AKS, and DevOps; designed scalable data workflows with ADF/ADLS/Synapse. Implemented Bayesian optimization and built Power BI dashboards; incorporated Explainable AI. Explored Agentic AI and Prompt Flow for investment research summaries. Delivered a cloud-native AI/ML platform powering data-driven financial insights.

AI/ML Engineer at Tesla

January 1, 2022 - January 1, 2022

Designed and deployed supervised/unsupervised ML models to classify high-risk customers, predict churn, and segment populations. Developed DL architectures (LSTMs, Transformers) for sequential activity modeling and engagement analytics. Applied NLP to extract churn indicators from support tickets and reviews. Built scalable ETL and data pipelines with Apache Spark on GCP; automated ML workflows with Airflow, MLflow, and Kubeflow; created real-time prediction APIs with FastAPI and containerized deployments on GKE. Leveraged GCP AI Platform for scalable model training and deployment; performed A/B testing and uplift analyses; implemented SHAP/LIME for model explainability. Collaborated with marketing/product teams to integrate insights into retention strategies, achieving significant reductions in churn and improved CLV.

Data Scientist at Oracle

May 1, 2020 - May 1, 2020

Developed an AI-driven demand forecasting platform for manufacturing and retail, integrating ML and statistical models to optimize procurement and inventory. Built supervised models (Scikit-learn, XGBoost) for demand forecasting and item classification; designed DL architectures (TensorFlow, PyTorch) for sequential purchase history modeling. Engineered scalable ETL pipelines with Spark/Hadoop and Pandas for ERP data and IoT streams. Conducted time-series forecasting (ARIMA, Prophet, LSTM) and unsupervised segmentation (K-Means, PCA). Delivered dashboards in Tableau/Power BI for executives; deployed anomaly detection pipelines and secure, scalable ML workflows on AWS. Implemented secure data handling with RBAC and encryption; exposed models as REST APIs.

Data Analyst at Solugenix

November 1, 2017 - November 1, 2017

Designed and optimized ETL pipelines with Python/SQL and Azure Data Factory, improving forecast accuracy and campaign ROI. Built real-time dashboards in Power BI/Tableau; applied statistical modeling and EDA to identify drivers of sales growth and retention. Automated reporting with Python scripts and Azure Logic Apps; implemented customer segmentation with Scikit-learn; integrated data pipelines into Azure Data Lake Storage for governance. Collaborated with sales/marketing to define KPIs and ensure alignment with revenue goals. Delivered data storytelling and visualization to executives; supported RBAC and encryption for data security.

Gen AI / Data Scientist at Broadridge Financials

February 1, 2022 - April 1, 2024

Developed an enterprise-grade advisory platform using LLMs and predictive models to generate personalized investment insights. Achieved a 29% improvement in recommendation accuracy and a 17% boost in portfolio returns. Built supervised ML models (scikit-learn, XGBoost) for predicting client preferences and risk tolerance; developed deep learning architectures (LSTMs, Transformers) to analyze sequential trading data. Fine-tuned LLMs on analyst reports and market feeds for sentiment/risk signal extraction. Created Azure Databricks (Spark) pipelines for processing market data; automated ML pipelines with Airflow, MLflow, Kubeflow; deployed real-time APIs with FastAPI; implemented encryption, RBAC, and audit logging. Explored vector databases (FAISS, Pinecone) for semantic search in investment research. Delivered an Azure-based GenAI advisory platform to boost advisor productivity and client satisfaction.

AI/ML Engineer at Tesla

June 1, 2020 - January 1, 2022

Designed supervised ML models (scikit-learn, XGBoost) to classify high-risk customers and improve churn detection. Built unsupervised clustering (k-means, DBSCAN) for granular customer segmentation. Developed DL models (LSTMs, Transformers) to capture sequential app usage patterns. Applied NLP pipelines (Hugging Face Transformers, BERT) to extract churn signals from support tickets and reviews. Engineered scalable data pipelines on GCP with Spark, Pandas, and NumPy; automated retraining and monitoring with Airflow, MLflow, and Kubeflow. Delivered real-time scoring APIs via FastAPI/Flask and deployed to GKE. Improved churn rate by 24% and CLV by 15%, while enabling rapid experimentation and deployment.

Data Scientist at Oracle

January 1, 2018 - May 1, 2020

Developed AI-driven demand forecasting for manufacturing and retail using ARIMA, Prophet, and LSTMs. Built scalable ETL pipelines with Spark/Hadoop to process ERP and IoT data. Implemented anomaly detection and time-series forecasting, improving inventory planning and procurement decisions. Exposed models via Flask/FastAPI APIs and deployed on AWS, with secure training and storage. Partnered with procurement and operations teams to align outputs with business needs, delivering a platform that reduced shortages and waste.

Data Analyst at Solugenix

August 1, 2015 - November 1, 2017

Designed and optimized ETL pipelines (Python, SQL, Azure Data Factory) to ingest CRM, ERP, and third-party data into centralized reporting. Improved forecast accuracy by 22% and campaign ROI by 15% by integrating Azure SQL Database with predictive analytics workflows. Built real-time dashboards (Power BI, Tableau) and automated reporting; performed customer segmentation and governance-enabled data lake integration. Collaborated with sales, marketing, and product teams to define KPIs and ensure data-driven decision-making.