Job Description

We are seeking a highly skilled AI/ML Expert to lead the design, implementation, and management of the AI/ML Repository and support the lifecycle management of analytical and machine learning models across the platform.

The successful candidate will be responsible for establishing architecture and governance for model storage, metadata management, model versioning, deployment, and integration with APIs and developer environments. The role requires expertise in machine learning operations (MLOps), data science platforms, AI governance, and scalable AI/ML infrastructure to support advanced analytics and innovation initiatives in accordance with SOW 4.1–4.8

The ideal candidate will possess deep technical expertise in artificial intelligence, machine learning, and analytical platforms, with experience delivering enterprise-scale AI solutions and data-driven initiatives.

Key Responsibilities

AI/ML Repository Architecture

• Design and implement the architecture for the AI/ML Repository to support model storage, sharing, deployment, and governance.
• Establish standards and frameworks for model lifecycle management across the platform.
• Define repository structures, metadata standards, version control mechanisms, and governance processes.
• Ensure scalability, security, and maintainability of AI/ML assets and analytical models.
• Develop strategies for model cataloguing, discoverability, and reuse.

Model Lifecycle Management

• Manage end-to-end machine learning model lifecycle processes from development to deployment and retirement.
• Implement model version control, monitoring, validation, and governance frameworks.
• Establish procedures for model testing, performance tracking, auditing, and continuous improvement.
• Support model deployment and integration into production environments.
• Ensure compliance with organizational policies, governance requirements, and AI best practices.

Analytics & Platform Integration

• Enable integration of analytical models and AI services with APIs, developer portals, and platform applications.
• Collaborate with development and integration teams to expose AI capabilities through secure APIs.
• Support deployment of predictive analytics and machine learning services across platform components.
• Ensure seamless interoperability between AI/ML services and enterprise systems.
• Provide guidance on AI service architecture and implementation approaches.

Data Science & AI Governance

• Define standards for model documentation, metadata management, and data lineage.
• Support responsible AI practices and governance frameworks.
• Establish guidelines for reproducibility, transparency, and explainability of machine learning models.
• Ensure compliance with data governance, privacy, and security requirements.
• Promote best practices in machine learning engineering and model management.

Technical Leadership

• Provide subject matter expertise in AI, machine learning, and advanced analytics.
• Guide teams on model development, deployment, testing, and optimization strategies.
• Mentor data scientists, machine learning engineers, and technical teams.
• Conduct architecture reviews and recommend improvements to AI/ML solutions.
• Evaluate emerging technologies, frameworks, and AI industry trends.

Project Delivery (SOW 4.1 – 4.8)

• Collaborate with solution architects, data engineers, software engineers, and stakeholders to deliver AI/ML workstreams.
• Support planning, implementation, testing, and deployment activities.
• Ensure AI/ML deliverables meet business, technical, security, and performance requirements.
• Contribute to project documentation, reporting, and knowledge transfer activities.

Basic Qualification

• Bachelor's or Master's Degree in Data Science, Artificial Intelligence, Computer Science, Information Technology, Statistics, Engineering, or a related discipline.