Mary Helena Rose A

Product Manager for Enterprise AI platforms

• Developing and delivering Cloud Native and Citizen Data Science Enterprise AI platforms
• Defining and communicating clear product vision and strategy
• Aligning product development with business goals and customer needs
• Developing and managing a roadmap that outlines feature priorities and timelines
• Overseeing the development of AI/ML platforms, ensuring timely and budget-compliant delivery
• Maintaining focus on quality, reliability, and security
• Implementing monitoring, analytics, and feedback loops to maintain/improve product performance and user satisfaction.
• Delivering Solutions for GenAI LLM use cases.

AI Platforms Lead

• Leading AI Platform engineers focused on developing cutting-edge data science AI/ML environments and workflows
• Providing infrastructure and platforms to support deployment and monitoring of ML solutions in production
• Optimizing solutions for performance and scalability
• Defining, deploying, and managing CI/CD processes and tools for ML/DL models
• Collaborating with various teams to create and implement cloud policies and governance for ML/DL model lifecycle.

AI/ML Ops Lead

• Leading AI Ops engineers focused on developing cutting-edge data science AI/ML environments and workflows
• Delivering tools through accessible development environments for Data Scientists and Machine Learning Engineers.
• Design and manage infrastructure, develop CI/CD pipelines, and automate the ML lifecycle, utilising cloud platforms and container orchestration tools.
• Work closely with data scientists and engineers to optimise models for performance and scalability, ensuring efficient and reliable data pipelines.
• Implement security best practices to protect data and models, ensuring compliance with data governance and regulatory requirements.
• Document processes and best practices, train and mentor team members, evaluate and integrate tools to enhance the ML Ops workflow and foster a culture of continuous improvement.

• Handling a team involved in machine learning model operationalization
• Applying model techniques on unseen data for real-time inference and model conversion based on deployment environment
• Using Python, Docker, Flask, and Kubernetes for model deployment.
• Evaluated suppliers to maintain cost controls and improve operations

• Leading a team creating data pipelines and managing data from different sources
• Scheduling Oozie Workflow and Coordinator Jobs
• Developing complex Hive queries for ETL processes
• Organizing, aggregating, querying, and analyzing data using PySpark and HiveQL.
• Evaluated various tools, technologies, and best practices for potential adoption in the company''s data engineering processes
• Explained data results and discussed how best to use data to support project objectives

• Application Support and Maintenance for Ford Europe’s Vehicle Accounting and Invoicing application (EUROVAC)
• Complete technical ownership of applications, including job monitoring, fixing production incidents, and coordinating with development teams for enhancements.

• Application Development for GE Commercial Finance
• Integration of GE Sanyo Credit system into GECL environment
• Analyzing requirements, preparing functional design documents, and coding in COBOL.
• Investigated system issues and implemented resolutions to reduce downtime

• Mainframe application maintenance for an American bank
• Handling change requests, preparing requirement and design documents, coding, and testing.
• Maintained existing software systems by identifying and correcting software defects
• Collaborated with clients to define solution requirements