AI Operations Manager Job Description and Role Profile

The AI Operations Manager role is suited to experienced professionals who combine technical understanding of machine learning models with operational leadership. Candidates should have a track record of managing model deployment, monitoring and governance activities and of working across data science, engineering and business teams. This job description provides a clear profile for recruiters, hiring managers and candidates seeking to define or apply for the position.

AI Operations Manager Job Profile

The AI Operations Manager is responsible for the operational delivery and reliability of AI and machine learning capabilities across the organisation. The role ensures that models are deployed, monitored and maintained in production to meet business needs while adhering to governance, compliance and quality standards.

The purpose of the role is to translate model development into repeatable, scalable operations, to reduce operational risk and to embed practices that ensure performance, reproducibility and accountability for AI systems.

AI Operations Manager Job Description

The AI Operations Manager leads the end to end operational lifecycle for AI solutions, coordinating cross-functional activity to deploy models safely and efficiently. This includes establishing processes for validation, release management, monitoring, incident response and continuous improvement. The role operates in a matrixed environment and requires collaboration with data scientists, data engineers, IT operations and business stakeholders.

Expectations include driving operational standards and metrics, ensuring data quality and model performance, and maintaining controls for compliance and ethical use. The role balances strategic planning with hands-on oversight of operational practices, prioritising reliability, scalability and clear documentation to support auditability and knowledge transfer.

AI Operations Manager: Duties and Responsibilities

• Manage the operational lifecycle of AI and machine learning models from deployment to retirement.
• Define and implement release management and change control processes for model updates.
• Establish monitoring frameworks to track model performance, data drift and production issues.
• Lead incident detection and response for model failures and degraded performance.
• Collaborate with data science and engineering teams to ensure reproducible model builds and versioning.
• Oversee data quality checks and pipelines that support reliable model inference.
• Develop and maintain documentation for operational procedures, runbooks and onboarding materials.
• Implement model validation and post-deployment validation practices to confirm expected behaviour.
• Coordinate model governance activities, including risk assessments and record keeping for audit purposes.
• Define operational metrics and reporting to measure availability, latency and business impact.
• Provide leadership to a team of operations engineers or MLOps specialists, including coaching and performance management.
• Work with stakeholders to prioritise operational work and align model operations with business objectives.
• Manage relationships with external partners or vendors involved in model hosting or operational services.
• Drive continuous improvement initiatives to improve automation, scalability and cost efficiency in AI operations.

AI Operations Manager: Requirements and Qualifications

• Bachelor's degree in computer science, engineering, data science or a related discipline; advanced degree preferred.
• Proven experience in AI operations, MLOps or production ML systems, typically 5+ years in relevant roles.
• Strong understanding of the machine learning model lifecycle, including deployment, monitoring and retraining practices.
• Experience designing and maintaining monitoring, alerting and incident management processes for models in production.
• Practical knowledge of data quality principles and approaches for ensuring reliable input to models.
• Competence in software development practices, version control and release management methodologies.
• Demonstrable ability to lead cross-functional teams and to communicate technical topics to non-technical stakeholders.
• Familiarity with governance, privacy and compliance considerations relevant to AI and data use.
• Analytical problem-solving skills and experience using metrics to drive operational decisions.
• Experience developing operational documentation, runbooks and standard operating procedures.
• Project management skills with the ability to prioritise and manage multiple concurrent operational initiatives.
• Strong interpersonal skills and the ability to influence stakeholders across the organisation.
• Continuous improvement mindset with a focus on automation, scalability and reliability.