• Guided Fortune 500 companies to devise, refine, and implement enterprise data, machine learning and analytics strategies. Facilitated C-level executive discussions on business vision, strategy and priorities. Lead design workshops with enterprise architects. Conducted threat modeling session to optimize security. Run build workshops to develop prototypes and MVPs.• Designed and implemented an AI-based intelligent Voice of Customer platform. Used natural language processing (NLP) to translate audio to text and sentiment analysis and generative AI for topic detection and text summarization. The impact of the solution was reduced number engagement center calls, increased CSAT scores, and reduced average length of calls.• Designed and implemented enterprise data platform based the on the data mesh architecture on AWS using S3, Snowflake, Power BI, QuickSight and Neptune Graph. The objective was to democratize data across the enterprise.• Architected and developed a highly scalable enterprise MLOps platform for enterprise AWS customers. Built using infrastructure as code AWS CDK and Python. Created custom SageMaker projects for self-serve and repeatable MLOps workflows, and integrated seamless CI/CD through GitHub Actions. The results were accelerated time-to-market, improved security, and enhanced governance of machine learning models.• Designed a data mesh for a 350PB data lake migration from on-prem to AWS using EC2, EMR, Apache Kafka, and S3.• Engaged with AWS service teams to influence product direction. Published thought leadership: “Building an Enterprise MLOps Platform on AWS with Amazon SageMaker, Github Actions and Amazon CDK.” “Build and Test Apache Amazon EMR Jobs locally using Visual Studio Code, PySpark and Amazon EMR Toolkit for VSCode.” “Build an Apache Spark Cluster from scratch on Amazon EC2 Step-By-Step.” “Benchmark Apache Spark jobs running on EMR using TeraSort, DFSIO, and TPC-DS.”

• Guided C-Level executives on data strategies roadmap. Led development teams in building analytics, business intelligence and machine learning for global customers. Implemented data lakes with Apache Spark, Databricks and Power BI. Wrote Apache Spark Park jobs with Scala. Achieved operational savings of $1B.• Implemented DevSecOps framework. Introduced threat modeling into the SDLC of development teams, and lead threat modeling sessions to identify attack vectors, and develop actions for mitigating risks.• Conducted technical training on Machine Learning, Python, C#, Microsoft Azure, Scala, Apache Spark, and Databricks.• Built time series forecasting machine learning model for demand planning, and real-time anomaly detection of financial transaction using Isolation Forest and Autoencoder with TensorFlow.

Architect new analytics platform. Design and implement a Business Intelligence solutions using the Microsoft data stack. Train the technical staff.

Conduct technical training in classroom setting based on Microsoft Official Curriculum courses.

Design and implement new enterprise Profit & Loss data warehouse system using the Microsoft data stack and Business Objects.

Developed enterprise web application used for global compliance department. The system handles the request and approval process of trading transactions for JPMorgan Chase employees, manages the required and completed training for each employee, and it is the central repository for trader licenses and registrations.

Provided technical leadership for a team of software engineers engaged in the development, integration and deployment of enterprise wide systems. Provided technical assistance to the Infrastructure Team responsible for architecting the web and database farms which host the various Intranet and Extranet sites. DBA Technical Lead and Data Architect. British Telecom (BT) intranet environment supports upwards of 180,000 users and over 10,000 web-enabled systems.

Led a team of software engineers engaged in the development, integration and deployment of enterprise systems. I led the development, architecture and database administrations. I researched new technologies to meet the user’s needs, improve team communication, and reduce maintenance time.