Role Purpose:

• Oversee AI Ops, ML Ops, and LLM Ops to ensure efficient and scalable operations of AI and machine learning models.
• Manage the entire model lifecycle, including development, deployment, monitoring, and maintenance.
• Ensure adherence to predefined Service Level Agreements (SLAs) for AI and ML operations.
• Develop and maintain CI/CD Ops pipelines for seamless integration and deployment of models.
• Implement and manage model registries for version control and governance.
• Establish and enforce coding checklists and best practices.
• Develop and automate testing frameworks to ensure model quality and reliability.
• Design and manage inference pipelines for real-time and batch predictions.
• Drive innovation by adopting emerging technologies (GenAI, AI, NLP) and accelerating product/service development through rapid prototyping and iterative methods.
• Align analytics innovation efforts with business strategy, IT strategy, and legal/regulatory requirements.
• Identify and develop advanced analytics capabilities and ecosystem partnerships in alignment with DnA strategy.

Key Responsibilities:

• Lead AI Ops, ML Ops, and LLM Ops to ensure the efficient and scalable operation of AI and machine learning models.
• Develop and manage the model lifecycle, including development, deployment, monitoring, and maintenance.
• Ensure adherence to predefined SLAs for AI and ML operations.
• Create and manage analytics product/services roadmaps from concept to launch.
• Develop and maintain CI/CD Ops pipelines for seamless integration and deployment of models.
• Implement and manage model registries for version control and governance.
• Establish and enforce coding checklists and best practices.
• Develop and automate testing frameworks to ensure model quality and reliability.
• Design and manage inference pipelines for real-time and batch predictions.
• Incubate and adopt emerging technologies (GenAI, AI, NLP) to accelerate product/service development through rapid prototyping and iterative methods.
• Align analytics innovation efforts with business strategy, IT strategy, and legal/regulatory requirements.
• Establish and update strategies, implementation plans, and value cases for emerging technologies.
• Drive innovation using appropriate people, processes, partners, and tools.
• Identify and develop advanced analytics capabilities and ecosystem partnerships in alignment with DnA strategy.
• Oversee end-to-end delivery of analytics services and products across cross-functional business areas.
• Serve as the point of escalation, review, and approval for key issues and decisions.
• Manage resource and capacity planning in line with business priorities and strategies.
• Foster continuous improvement within the team.
• Decide on program timelines, governance, and deployment strategies.

Key performance indicators:

• Achieved targets in Enterprise business case contribution, KPIs, customer satisfaction, and innovation measures
• Delivery on agreed KPIs including business impact -Launch of innovative technology solutions across Novartis at scale
• Business impact and value generated from DDIT solutions -Adoption and development of Agile Productization and DevOps practices -Operations stability and effective risk management -Feedback on customer experience -Applications adherence to ISC requirements and are audit ready.
• Business capability, vision & strategy clearly defined, communicated, and executed, well aligned to business strategy and Enterprise IT strategy, and providing a competitive advantage to Novartis
• Role model with the highest standards of professional conduct in leading the business capability area in line with the new IT operating model
• Deployment of digital platforms and services at scale to deliver the digital strategy

Skills and Experience:

• Demonstrated experience in Budget Management. Business Acumen. Performance Management. Planning. Project Management, , Risk Management. Service Delivery Management and stakeholder management
• Strong understanding of AI Ops, ML Ops, and LLM Ops.
• Experience in developing and managing the model lifecycle, including deployment and maintenance.
• Proficiency in managing operations with predefined SLAs.
• Expertise in CI/CD Ops pipelines development.
• Experience with model registry and management.
• Knowledge of coding checklists and best practices.
• Proficiency in developing and automating testing frameworks.
• Experience in designing and managing inference pipelines.
• Production experience with commercial and open-source ML platforms.
• Strong knowledge of AWS, Databricks, and Snowflake service offerings.
• Ability to collaborate with business teams to gather requirements, groom product backlogs, and drive delivery.
• Agile delivery experience managing multiple concurrent delivery cycles.
• Solid foundation in CRISP analytical life cycle management.
• Strong leadership skills with the ability to build high-performing teams.
• Excellent vendor management and IT governance skills.
• Innovative and analytical mindset with a focus on continuous improvement. Emerging Technology Monitoring, Consulting, Influencing & persuading, Unbossed Leadership, IT governance, Building High Performing Teams, Vendor Management, Innovative & Analytical Technologies.

• Strong understanding of descriptive vs. prescriptive Analytical frameworks.
• Strong knowledge of visualization platforms and project life cycle management, including Power BI, Qlik, and MicroStrategy.
• Significant production experience addressing visualization platform and data pipeline performance constraints.
• Strong analytical and problem-solving skills, effective communication, and the ability to influence and collaborate with cross-functional teams.

Languages:

• English.