Ai Delivery Director

Key Responsibilites:

Lead the end-to-end AI delivery function , ensuring consistent, high-quality execution across multiple squads and business domains

• Oversee the full lifecycle of AI initiatives , from use case identification and prioritization through to deployment, scaling, and continuous optimization
• Establish and institutionalize delivery governance frameworks , including clear operating models, performance cadence, and accountability structures
• Build and lead high-performing, cross-functional teams , including engineering, data science, and product, operating through layered leadership
• Define and implement scalable AI platform architectures , enabling reuse, interoperability, and efficient deployment across the enterprise
• Drive the adoption of enterprise-grade MLOps standards , including CI/CD pipelines, model versioning, automated testing, and controlled release processes
• Ensure robust AI lifecycle management and model governance , including monitoring, drift detection, retraining strategies, and incident management protocols
• Standardize delivery through platform-based approaches , including shared data pipelines, reusable components, and consistent model serving patterns
• Collaborate closely with business and technology leadership to ensure alignment between AI delivery and strategic priorities
• Define and track key performance indicators related to delivery efficiency, model performance, and business value realization
• Ensure adherence to responsible AI principles , including auditability, transparency, and regulatory alignment

Requirments:

• Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related discipline; advanced degree preferred
• Minimum of 10 years of experience in AI, machine learning, advanced analytics, or software engineering , with a strong record of delivering solutions in production environments
• Demonstrated success in leading large-scale AI delivery functions , including managing managers and cross-functional teams within complex organizations
• Deep expertise in MLOps and ML lifecycle management , including model versioning, CI/CD, monitoring, and continuous improvement practices
• Strong understanding of enterprise AI architectures , including data pipelines, feature stores, model serving, and hybrid cloud/on-premise environments
• Proven ability to design and scale platform-based AI capabilities , enabling reuse and consistency across multiple use cases
• Experience operating within structured environments requiring governance, controls, and auditability , preferably within regulated or large-scale industries
• Strong stakeholder management and communication capabilities , with the ability to engage senior leadership and translate business objectives into execution
• Demonstrated ability to balance strategic thinking with hands-on delivery oversight , ensuring both direction and execution excellence