Program Manager – Machine Learning & AI

About the Role

• We are looking for a highly experienced Program Manager to lead complex, high-impact Machine Learning (ML) and Artificial Intelligence (AI) programs across the organization. This role requires deep technical fluency, strong execution rigor, and the ability to orchestrate large-scale AI initiatives spanning research, engineering, infrastructure, and product.
• You will play a critical role in scaling AI capabilities, operationalizing ML systems, and driving cross-functional alignment in a fast-paced environment.

Key Responsibilities

• Own end-to-end delivery of complex AI/ML programs, from ideation through production deployment and scaling
• Drive program governance across multiple ML teams, ensuring alignment on priorities, dependencies, and outcomes
• Partner with Data Science, ML Engineering, Infrastructure, and Product to operationalize models into production systems
• Establish and enforce best practices across the ML lifecycle (data pipelines, training, validation, deployment, monitoring)
• Build program frameworks for MLOps maturity, model lifecycle governance, and reproducibility standards
• Manage risks related to data quality, model performance drift, and production reliability
• Lead executive-level reporting, including KPIs, model impact, and ROI metrics
• Enable cross-team collaboration across geographically distributed teams

Required Qualifications

• Bachelor’s degree in Computer Science, Engineering, Data Science, or related field (Master’s or MBA preferred)
• 7+ years of program management experience in highly technical environments
• 3+ years directly supporting Machine Learning or AI-driven products
• Strong understanding of ML workflows: data ingestion, feature engineering, training, evaluation, deployment, and monitoring
• Proven track record managing large-scale, cross-functional technical programs
• Excellent stakeholder management and executive communication skills

Preferred

• Hands-on experience with MLOps frameworks (e.g., Kubeflow, MLflow, TFX) and production ML pipelines
• Exposure to LLMOps (Large Language Model Operations), including prompt versioning, model evaluation, and safety guardrails
• Experience implementing Responsible AI / Model Governance frameworks (bias detection, explainability, auditability)
• Familiarity with feature stores (e.g., Feast) and real-time inference systems
• Strong understanding of distributed systems and data platforms (e.g., Spark, Kafka)
• Experience with cloud-based ML platforms (AWS SageMaker, Google Cloud Platform Vertex AI, Azure ML)