Data Sceintist - AI/ML

?Vasantha T

Senior AI/ML Engineer Generative AI Data Scientist AWS Azure GCP

*****************@*****.*** +1-314-***-****

PROFESSIONAL SUMMARY

? Senior AI/ML Engineer and Data Science professional with 10+ years of experience delivering advanced analytics, Machine Learning, Generative AI, and data-driven solutions across Healthcare, Banking, Government, Retail, and Technology domains.

? Strong expertise in Python, SQL, PySpark, Scikit-Learn, TensorFlow, PyTorch, FastAPI, Databricks, Snowflake, and cloud platforms including AWS, Azure, and GCP for building scalable AI and data solutions.

? Hands-on experience developing Generative AI applications using GPT-4, LangChain, LangGraph, Retrieval-Augmented Generation (RAG), prompt engineering, vector databases, semantic search, and agentic AI workflows.

? Experienced in designing end-to-end data pipelines, data integration frameworks, feature engineering processes, ETL/ELT solutions, and real-time analytics platforms for large-scale structured and unstructured datasets.

? Strong background in Machine Learning, Deep Learning, NLP, Classification, Clustering, Regression, Forecasting, Anomaly Detection, Recommendation Systems, and Predictive Analytics to solve complex business problems.

? Extensive experience working with healthcare data including Claims, EHR, Population Health, Risk Stratification, Care Management, Clinical Documents, and HIPAA-compliant analytics solutions.

? Proven expertise in banking and financial services projects involving fraud detection, risk modeling, customer analytics, transaction monitoring, regulatory compliance, and operational intelligence solutions.

? Skilled in building production-ready AI/ML platforms with model deployment, API development, MLflow, Docker, Kubernetes, monitoring, observability, governance, automated retraining, and CI/CD implementation.

? Strong experience creating executive dashboards, self-service analytics solutions, KPI reporting, and business intelligence applications using Power BI, Tableau, SQL, and enterprise reporting platforms.

? Excellent communication and stakeholder management skills with proven ability to collaborate with business leaders, clinicians, product owners, architects, engineers, and cross-functional teams to deliver high-impact AI and analytics solutions.

TECHNICAL SKILLS

Category

Skills & Technologies

AI, Machine Learning & Generative AI

GPT-4, GPT-3.5, Hugging Face Transformers, Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), LangChain, LangGraph, Agentic AI Workflows, Prompt Engineering, Structured Output Parsing, LLM Chaining & Orchestration, OpenAI Embeddings, Sentence Transformers, Semantic Search, FAISS, Pinecone, Chroma, NLP, Text Classification, Named Entity Recognition (NER), Clustering, Classification, Regression, Time Series Forecasting (ARIMA, Prophet), Anomaly Detection, A/B Testing, Feature Engineering, Model Evaluation, Deep Learning (CNN, RNN, LSTM)

Programming Languages

Python, SQL, R, PySpark

Python Libraries & Frameworks

Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, FastAPI, Flask

Data Processing & Big Data

Apache Spark, Databricks, Hadoop (HDFS, Hive), Data Transformation, ETL/ELT Processing

Cloud Platforms

AWS (SageMaker, S3, EC2, Glue, Lambda, Redshift, Athena), Azure (Azure ML, Azure Data Factory, Azure Synapse Analytics, Azure Data Lake Storage), GCP (BigQuery)

Databases

PostgreSQL, MySQL, SQL Server, MongoDB, NoSQL Databases

Data Warehousing

Snowflake, Redshift, Azure Synapse Analytics, BigQuery

MLOps & Deployment

MLflow, Docker, Kubernetes, Apache Airflow, CI/CD Pipelines, Model Deployment, Model Monitoring, Batch Inference, Real-Time Inference, Output Validation, Logging, Observability

API & Integration Technologies

REST APIs, FastAPI, Flask, API Integrations, Real-Time Data Processing

SQL & Data Engineering

Advanced SQL, Joins, Window Functions, CTEs, Query Optimization, Data Modeling, Data Integration, Data Validation, Data Quality Management

Visualization & Reporting Tools

Power BI (DAX, Data Modeling), Tableau, Looker, Excel (Power Query, Advanced Formulas), Jupyter Notebook

Version Control & Development Tools

Git, VS Code, Jupyter Notebook

Healthcare Domain Expertise

Claims Analytics, EHR Analytics, Population Health Management, Risk Stratification, Care Management, Clinical Analytics, HIPAA Compliance

Financial Services Expertise

Fraud Detection, Risk Modeling, Transaction Analytics, Customer Analytics, Regulatory Reporting, Financial Data Analysis

Retail Domain Expertise

Demand Forecasting, Inventory Optimization, Customer Behavior Analysis, Sales Analytics, Supply Chain Analytics

Methodologies

Machine Learning Lifecycle Management, Predictive Analytics, Statistical Analysis, Data Governance, Agile, Scrum, Stakeholder Management, Requirements Gathering, Production Support

PROFESSIONAL EXPERIENCE

TD Bank Charlotte, NC Sep 2025 ? Present

Sr. AI and Machine Learning Engineer

? Partnered with business, risk, and compliance teams to understand analytical requirements and deliver data-driven solutions.

? Built machine learning models to detect fraud, identify anomalies, and improve customer risk assessment processes.

? Developed data pipelines to collect, transform, and prepare large volumes of financial data for analytics and reporting.

? Created predictive models that supported customer behavior analysis, operational forecasting, and business planning.

? Built scalable data pipelines using Azure Data Factory and Azure Data Lake Storage to process financial and customer data, supporting enterprise analytics and regulatory reporting.

? Leveraged Azure Machine Learning and Azure Synapse Analytics to deploy and monitor fraud detection, credit risk, and predictive analytics models, improving business decision-making and operational efficiency.

? Automated data integration and analytics workflows using Azure Data Factory, Azure Synapse Analytics, and Azure Data Lake Storage, reducing processing time and enabling near real-time risk and customer insights.

? Developed AI-powered document search and knowledge retrieval solutions using large language models and vector search technologies.

? Worked with structured and unstructured data sources to generate actionable insights for business stakeholders.

? Designed automated workflows that reduced manual effort and improved the efficiency of model deployment activities.

? Conducted data exploration and feature engineering to improve model accuracy and business relevance.

? Collaborated with application development teams to integrate machine learning services into enterprise platforms.

? Built dashboards and reports to track model performance, business KPIs, and operational metrics.

? Performed model validation, testing, and performance monitoring to ensure reliable production results.

? Assisted with audit requests, model documentation, and governance activities required in regulated banking environments.

? Optimized data processing jobs and improved the performance of large-scale analytics workloads.

? Supported stakeholders by providing analytical insights and recommendations that helped drive business decisions.

Environment: Python (Pandas, NumPy, Scikit-learn), PyTorch, TensorFlow, SQL, PySpark, Apache Spark, Azure Machine Learning, Azure Data Factory, Azure Synapse Analytics, Azure Data Lake Storage, Databricks, OpenAI GPT-4, LangChain, FAISS, Pinecone, MLflow, Docker, Apache Airflow, REST APIs, Tableau, Power BI, PostgreSQL, MySQL

Molina Healthcare Miami, FL May 2023 ? Aug 2025

Senior Data Scientist ? Gen AI and LLMs

? Worked closely with care management and clinical teams to understand business challenges and identify opportunities where AI and machine learning could improve patient outcomes.

? Developed Generative AI solutions using GPT models to summarize patient histories, identify care gaps, and support clinical decision-making processes.

? Built retrieval-augmented generation (RAG) applications that allowed users to quickly search and retrieve information from clinical documents and care plans.

? Designed and implemented healthcare data pipelines using Azure Data Factory and Azure Data Lake Storage to process claims, provider, and EHR data, enabling scalable analytics and AI-driven insights.

? Leveraged Azure Machine Learning and Azure Synapse Analytics to develop, deploy, and monitor predictive models and Generative AI solutions that supported population health management, risk stratification, and clinical decision-making.

? Designed machine learning models to identify high-risk patients and support population health management initiatives.

? Created scalable data pipelines to process claims, provider, and EHR data from multiple healthcare systems.

? Improved data quality by validating incoming datasets and standardizing information before it was used for reporting and model development.

? Worked with healthcare stakeholders to refine AI-generated responses and ensure outputs aligned with business and clinical requirements.

? Developed APIs and services that enabled healthcare applications to consume predictions and AI-generated insights in real time.

? Monitored model performance and addressed issues related to prediction accuracy, drift, and changing business requirements.

? Implemented security and compliance controls to ensure sensitive healthcare information was handled according to HIPAA standards.

? Collaborated with data engineers to optimize data processing workflows and improve system performance.

? Participated in architecture discussions and helped define best practices for enterprise AI adoption.

? Supported production deployments and resolved issues impacting AI applications and analytics platforms.

? Mentored junior team members and shared knowledge on machine learning, Generative AI, and cloud-based analytics solutions.

Environment: Python (Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch), SQL (T-SQL, PostgreSQL), PySpark, Azure ML, Azure Data Factory, Azure Synapse, Azure Data Lake Storage, Databricks, OpenAI GPT-4, LangChain, LangGraph, FAISS, FastAPI, MLflow, Docker, Power BI, REST APIs, Git, HIPAA-Compliant Data Handling

State of NY New York, NY Oct 2021 ? April 2023

Data Engineer

? Gathered reporting and analytics requirements from business users and translated them into practical data solutions.

? Analyzed large datasets to identify trends, patterns, and opportunities for operational improvements.

? Built and maintained dashboards that provided leadership teams with visibility into key performance indicators.

? Designed and optimized data pipelines using GCP Dataflow and BigQuery to process large volumes of operational and reporting data, improving data availability and reporting performance.

? Leveraged Vertex AI and Cloud Functions to support machine learning model deployment, automated data processing workflows, and scalable analytics solutions for business users.

? Supported machine learning initiatives by preparing datasets, validating model outputs, and assisting with fraud detection analytics.

? Developed automated data workflows using Airflow and cloud-based services to streamline reporting and data processing activities.

? Collaborated with data science teams to deploy analytical models and monitor performance using MLflow and cloud-native tools.

? Developed SQL queries and reporting solutions to support day-to-day business operations.

? Created data validation processes that improved the accuracy and consistency of reporting outputs.

? Assisted with fraud detection and risk analysis initiatives using statistical and machine learning techniques.

? Automated recurring reports and data preparation activities to reduce manual effort and improve efficiency.

? Worked with multiple source systems to collect, cleanse, and integrate data into centralized reporting platforms.

? Conducted root cause analysis on data quality issues and implemented corrective actions.

? Collaborated with technical and business teams to resolve reporting challenges and improve data accessibility.

? Supported data governance initiatives by documenting business rules, data definitions, and reporting standards.

? Performed ad hoc analysis to support management requests and strategic planning efforts.

? Assisted in testing and validating new reports, dashboards, and analytical solutions before deployment.

? Provided ongoing support for reporting environments and analytical applications used across departments.

Environment: Python (Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch), SQL, PySpark, Apache Spark, GCP (Vertex AI, Cloud Storage, Compute Engine, Dataflow, Cloud Functions, BigQuery), MLflow, Docker, Apache Airflow, FastAPI, Flask, REST APIs, Power BI, SQL Server

Walmart Houston, TX June 2019 ? Sep 2021

Business Data Analyst / Data Engineer

? Worked with merchandising, inventory, and operations teams to understand business needs and support data-driven decisions.

? Analyzed sales, inventory, and customer purchasing data to identify trends and business opportunities.

? Developed forecasting models that helped improve inventory planning and product availability across stores.

? Created dashboards and reports that provided visibility into sales performance and operational metrics.

? Utilized AWS S3 and AWS Glue to ingest, transform, and manage large volumes of sales, inventory, and customer transaction data, enabling scalable reporting and analytics across retail operations.

? Supported inventory forecasting and sales analytics initiatives by leveraging AWS SageMaker for predictive modeling and AWS Lambda to automate data processing and reporting workflows.

? Improved inventory optimization by identifying stock shortages, excess inventory, and replenishment gaps, helping business teams enhance product availability and reduce stock-related issues.

? Performed customer segmentation analysis to better understand shopping behavior and purchasing patterns, enabling targeted marketing strategies and improved customer engagement.

? Evaluated the effectiveness of promotional campaigns by analyzing sales trends and customer response data, providing recommendations that supported future marketing and merchandising decisions.

? Automated reporting processes using SQL and Python, reducing manual reporting efforts and improving the accuracy, consistency, and timeliness of business insights.

? Collaborated with supply chain teams to analyze distribution, fulfillment, and inventory movement data, identifying opportunities to improve operational efficiency and reduce delivery bottlenecks.

? Investigated data discrepancies and partnered with source system owners to improve data quality, ensuring accurate reporting and reliable analytical outcomes across business functions.

? Prepared analytical reports and executive presentations that communicated key business trends, operational performance metrics, and actionable recommendations to leadership teams.

? Assisted with data integration efforts involving POS systems, warehouse management systems, and supplier data sources, supporting enterprise reporting and analytics initiatives.

? Maintained documentation for reports, dashboards, business processes, and data definitions to support ongoing operations, knowledge sharing, and reporting consistency.

? Provided regular insights and recommendations based on sales, inventory, and customer analytics, helping business stakeholders improve operational performance and support strategic planning initiatives.

Environment: SQL, Python (Pandas, NumPy), R, Excel (Advanced, Pivot Tables, Power Query), Tableau, Walmart Retail Link, POS Systems, Data Warehousing, AWS (S3, Glue, Lambda, SageMaker), Sales Analytics, Inventory Forecasting

Tech Mahindra India May 2016 ? Feb 2019

Data Analyst

? Worked with business users to gather reporting requirements and understand operational challenges.

? Developed complex SQL queries, stored procedures, and reporting solutions to support finance, operations, and management teams, enabling accurate business reporting and data-driven decision-making.

? Created data extraction, transformation, and validation processes to consolidate information from multiple source systems, improving data consistency and reducing manual reporting efforts.

? Assisted in data integration and transformation activities using AWS Glue to consolidate data from multiple business applications and improve reporting efficiency across departments.

? Built dashboards and visual reports using Tableau, Power BI, and Excel, providing stakeholders with visibility into key performance indicators, operational metrics, and business trends.

? Performed data cleansing and validation activities to improve reporting accuracy and ensure the reliability of analytical outputs.

? Automated recurring reporting tasks using SQL, Python, and Excel-based solutions, reducing manual effort and improving operational efficiency.

? Assisted in troubleshooting reporting issues and resolving data inconsistencies across multiple reporting systems and source applications.

? Conducted trend analysis and generated insights that supported business planning, operational improvements, and management decision-making initiatives.

? Collaborated with development teams during system enhancements and data migration projects to ensure data quality, reporting continuity, and successful implementation of business requirements.

? Maintained documentation for data sources, transformation logic, reporting processes, and business rules to support ongoing operations and knowledge transfer.

? Worked with large datasets to identify trends, anomalies, and process improvement opportunities, delivering actionable insights that supported operational efficiency and business performance.

? Supported user acceptance testing activities for reporting and analytics solutions, ensuring business requirements were met and validated before deployment.

? Provided ongoing production support for business intelligence and reporting environments.

? Supported data migration initiatives using AWS S3 for secure storage and management of large business datasets, improving data accessibility and supporting enterprise reporting requirements.

? Assisted stakeholders with ad hoc reporting requests and delivered timely analytical support to address evolving business needs.

Environment: SQL (MySQL, SQL Server), Python (Pandas, NumPy), Excel (Pivot Tables, VLOOKUP), Tableau, Power BI, Jupyter Notebook, Git, ETL Processes, Data Cleaning, Reporting and Dashboard Development, AWS (S3, Glue)

Contact this candidate