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Ganesh kumar - Senior GEN AI ENGINEER - ML ENGINEER - DATA SCIENCE

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Location: Dallas, Texas, USA

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GANESH KUMAR +1 (945) 264-5336 [email protected] Senior GEN AI ENGINEER - ML ENGINEER - DATA SCIENCE Professional Summary: Senior Gen AI Engineer, Machine Learning Engineer, and Data Scientist with 9+ years of experience delivering data-driven and AI-powered solutions across Banking, Healthcare, Insurance, and Public Sector domains, supporting both strategic decision-making and operational efficiency. Extensive experience translating complex business problems into scalable AI, ML, and analytics solutions through close collaboration with business stakeholders, product owners, and enterprise architecture teams. Strong expertise in end-to-end machine learning lifecycle, including data ingestion, feature engineering, model training, evaluation, deployment, monitoring, and optimization in production environments. Proven ability to design and deploy Gen AI and predictive analytics solutions for fraud detection, risk scoring, forecasting, compliance automation, and customer behavior analysis. Hands-on experience working with large, complex datasets using SQL, Python, Spark, and cloud-native data platforms to ensure data accuracy, integrity, and performance. Advanced knowledge of cloud analytics ecosystems across Azure, AWS, and GCP, supporting enterprise-scale analytics, ML workloads, and data migrations. Strong background in regulatory and compliance-driven environments, ensuring analytics and AI solutions meet SOX, HIPAA, Basel III, Dodd-Frank, GDPR, and SOC 2 standards. Experienced in building executive and operational dashboards using Power BI, Tableau, and Qlik Sense to deliver actionable insights for technical and non-technical stakeholders. Demonstrated leadership in Agile and hybrid delivery models, driving sprint planning, backlog grooming, UAT coordination, and cross-team alignment. Expertise in data governance, access control, and security frameworks, implementing RBAC, encryption, and audit controls across cloud platforms. Proven success in modernizing legacy systems through cloud migrations, automated ETL pipelines, and AI-driven reporting frameworks. Strong communicator capable of bridging gaps between business users, data engineers, ML engineers, and QA teams. Experience integrating AI and analytics solutions with enterprise applications through REST APIs and secure integration layers. Adept at working in onshore-offshore delivery models, coordinating with managed service providers and global teams. Recognized for delivering high-impact, production-ready AI solutions that balance technical innovation with regulatory and business constraints. Technical Skills: Data Analysis & Business Intelligence: SQL (MS SQL Server, MySQL, PostgreSQL, Azure SQL), Power BI, Tableau, Qlik Sense, Data Visualization & Reporting, A/B Testing, Statistical Analysis, Python, R, MATLAB Cloud Computing & Big Data: AWS (S3, Redshift, Glue, Lambda, SageMaker, Athena), Azure (Synapse, Data Factory, Databricks, Purview), GCP Big Query, Apache Spark, Hadoop, Databricks, Cloud Data Migrations & Governance ETL & Data Engineering: Azure Data Factory, AWS Glue, Apache NiFi, Informatica, Data Warehousing (AWS Redshift, Azure Synapse, GCP Big Query), API Development & Integration (Python, Azure API Management, RESTful APIs) Predictive Modeling & Machine Learning: Python (Pandas, NumPy, Scikit-learn, PySpark), ML Platforms (AWS SageMaker, Azure ML, MLflow), Use Cases: Fraud Detection, Credit Risk Analysis, Customer Segmentation Enterprise Content & Data Management: OpenText Content Suite, OpenText Extended ECM, Data Governance & Compliance (GDPR, CCPA, HIPAA, SOC 2), Data Security (RBAC, Transparent Data Encryption) Agile & Project Management: Agile/Scrum (Sprint Planning, Backlog Grooming, Stakeholder Engagement), CI/CD & DevOps (Azure DevOps, GitHub, AWS Code Commit), Regulatory Compliance Reporting (Basel III, Dodd-Frank, HIPAA) Advanced Excel: Pivot Tables, Conditional Formatting, Data Validation, Macros, VBA Scripting Issue Tracking & Test Management: JIRA, Azure DevOps, Microsoft Test Manager (MTM), TestRail, UAT Planning, Test Case Design Banking & Regulatory Expertise: Core Banking Conversions (Data Migration, Integration, Reporting), Basel III, Dodd-Frank, SOX, and regulatory compliance reporting, Core Banking Modules (Deposits, Loans, Payments, Ancillary) PROFESSIONALEXPERIENCE: Client: Citi Bank, Irving, TX Apr 2024 to till date Role: Senior Gen AI Engineer, ML Engineer Roles & Responsibilities: Led the design and implementation of Gen AI and ML-driven analytics solutions supporting core banking functions such as reconciliation, liquidity analysis, loan portfolio monitoring, and risk reporting. Collaborated extensively with banking stakeholders through workshops and interviews to gather, analyze, and document business requirements, translating them into BRDs, FRDs, and analytics specifications. Designed and implemented fraud detection and anomaly detection models using transactional banking data to identify suspicious patterns and improve early risk identification. Developed and optimized complex SQL queries and data models on Azure Synapse, enabling accurate financial reconciliation and improving transaction traceability across systems. Built enterprise-grade Power BI and Qlik Sense dashboards to provide real-time visibility into financial performance, regulatory metrics, and operational KPIs. Automated end-to-end ETL pipelines using Azure Data Factory to ingest data from multiple internal banking systems, significantly reducing manual effort and reporting latency. Implemented secure, real-time API integrations using Azure API Management to support data exchange between internal systems and external platforms. Played a key role in Basel III and Dodd-Frank compliance reporting, automating regulatory submissions and enhancing audit readiness. Participated actively in UAT cycles, validating ML outputs, reconciliation logic, and reporting accuracy in collaboration with business users. Logged, tracked, and managed defects and change requests using Azure DevOps and JIRA, ensuring stability of production analytics pipelines. Enforced data governance and security standards by implementing RBAC, data classification, and lineage using Azure Purview and Collibra. Supported core banking transformation initiatives, aligning analytics and AI solutions with enterprise architecture and regulatory requirements. Client: New York State Office (ITS), New York, NY Dec 2021 to Dec 2023 Role: Senior Gen AI Engineer Roles & Responsibilities: Designed and delivered AI-driven analytics solutions supporting state-level insurance and public sector programs, focusing on risk assessment, utilization analysis, and compliance reporting. Applied machine learning models and A/B testing methodologies to evaluate and optimize policyholder engagement and service delivery outcomes. Worked closely with government stakeholders to translate policy, regulatory, and operational requirements into scalable analytics and ML frameworks. Built and optimized SQL-based analytical models in AWS Redshift, supporting insurance claims analysis and performance reporting. Developed predictive models to identify risk trends, cost drivers, and utilization patterns across insurance datasets. Engineered ETL pipelines using AWS Glue and Lambda, ensuring reliable and timely ingestion of insurance and public sector data. Delivered executive-level dashboards using Tableau, enabling leadership to track insurance program performance and compliance metrics. Ensured strict adherence to HIPAA and public sector data regulations, embedding security and privacy controls into analytics workflows. Automated regulatory and compliance reporting using AWS Athena and BI tools, reducing manual reporting overhead. Implemented data governance, access controls, and encryption using AWS-native security services. Operated in Agile delivery environments, supporting sprint planning, backlog prioritization, and stakeholder reviews. Served as a key liaison between policy teams, technical teams, and data engineering groups, ensuring analytics solutions met public sector objectives. Client: Cigna Healthcare, Bloomfield, CT Feb 2020 to Nov 2021 Role: Data Engineer Roles & Responsibilities: Designed, developed, and maintained end-to-end healthcare data pipelines to ingest, transform, and process large volumes of claims, eligibility, provider, and member data from multiple source systems, ensuring consistent data flow into enterprise reporting and analytics platforms. Built and optimized high-performance ETL workflows using industry-standard data integration tools and SQL-based transformations to standardize healthcare datasets, resolve schema inconsistencies, and align data across claims processing, enrollment, and care management systems. Developed healthcare-specific data models to support medical claims adjudication, utilization analysis, member risk profiling, and provider performance reporting, enabling downstream teams to perform accurate operational, clinical, and financial analysis. Implemented robust data quality frameworks to validate healthcare data accuracy, completeness, and timeliness, including checks for duplicate claims, eligibility gaps, invalid procedure codes, and provider mismatches before data was consumed for reporting or compliance purposes. Performed extensive data cleansing and normalization of healthcare coding systems such as ICD-10, CPT, and HCPCS, ensuring consistency across historical and incoming datasets and improving the reliability of analytics related to utilization, cost, and outcomes. Engineered secure integrations with EHR systems, payer platforms, and third-party healthcare vendors using batch files, APIs, and structured data exchanges, enabling seamless movement of clinical and operational data across the healthcare ecosystem. Created SQL-driven reconciliation and exception reports to track discrepancies across claims lifecycle stages, member enrollment periods, and provider contracts, helping operations teams quickly identify and resolve data issues impacting payments and reporting. Supported regulatory and compliance reporting initiatives by preparing validated datasets aligned with healthcare regulations, audit requirements, and internal governance standards, ensuring accurate submissions for state and federal healthcare programs. Collaborated closely with healthcare business analysts, compliance teams, and reporting stakeholders to translate complex healthcare requirements into scalable data engineering solutions that supported claims analytics, care coordination, and performance monitoring. Led UAT and data validation activities for healthcare ETL pipelines and reporting datasets, partnering with QA and business users to verify data accuracy, performance, and adherence to functional and regulatory expectations before production release. Implemented data governance and access control practices to safeguard protected health information (PHI), ensuring datasets followed HIPAA guidelines, audit trails were maintained, and sensitive data access was restricted based on user roles and business need. Continuously optimized data processing performance and scalability, tuning SQL queries, ETL workflows, and data structures to handle growing healthcare data volumes while reducing processing time and improving reliability for downstream analytics and reporting teams. Client: Target Corporation, Bangalore, India Jan 2016 to Nov 2019 Role: Data Analyst Roles& Responsibilities: Delivered comprehensive analytics solutions supporting retail financial performance, inventory trends, and investment decision-making, enabling leadership to monitor key business metrics in real time. Designed, developed, and maintained interactive Power BI dashboards to track revenue trends, cost structures, and operational KPIs across multiple business units. Built and optimized complex SQL queries and data models to analyze high-volume transactional and financial data, ensuring accuracy and performance. Developed automated ETL workflows using Azure Data Factory and SQL-based transformations to streamline data ingestion from multiple retail systems. Conducted exploratory data analysis (EDA) to uncover purchasing behavior patterns, seasonal trends, and demand fluctuations that informed strategic planning. Applied statistical analysis and forecasting techniques using Python and R to support budget planning, sales forecasting, and risk assessment. Supported trade processing, reconciliation, and financial validation by verifying data consistency across upstream and downstream systems. Created ad hoc analytical reports to respond quickly to evolving business questions from finance, merchandising, and operations teams. Worked closely with stakeholders to translate business requirements into analytical solutions, ensuring dashboards and reports aligned with real operational needs. Automated data refresh and reporting schedules to provide near real-time insights without manual intervention. Implemented row-level security and role-based access controls within BI tools to protect sensitive financial and operational data. Collaborated in Agile environments, participating in sprint planning, backlog refinement, and cross-functional reviews to continuously enhance analytics deliverables. 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