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AI Operations Manager job description
An AI Operations Manager oversees the deployment, monitoring, and optimization of artificial intelligence systems within an organization, ensuring they operate efficiently and deliver maximum business value by bridging the gap between technical AI development and practical business applications.
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What is a AI Operations Manager?
An AI Operations Manager is a specialized leadership role responsible for the end-to-end operational management of artificial intelligence systems and infrastructure. This professional ensures that AI models, data pipelines, and machine learning operations (MLOps) run smoothly, reliably, and at scale. They combine technical expertise with strategic oversight to maintain AI system performance, manage resources, and align AI operations with organizational objectives. The role has emerged as critical in organizations that rely heavily on AI, requiring a unique blend of AI knowledge, operational excellence, and business acumen to ensure AI investments deliver tangible returns.
What does a AI Operations Manager do?
An AI Operations Manager oversees the daily functioning of AI systems, monitoring performance metrics and ensuring system reliability. They manage MLOps pipelines, coordinating between data scientists, engineers, and business stakeholders to deploy and maintain AI models effectively. Their responsibilities include implementing monitoring systems to track model performance and data quality, optimizing AI infrastructure for cost efficiency and scalability, and developing incident response protocols for AI system failures. They also establish governance frameworks for AI operations, manage resource allocation for AI projects, and continuously work to improve AI system performance and alignment with business goals through iterative optimization processes.
Job Overview
The AI Operations Manager will lead the operational strategy and execution of AI systems and machine learning infrastructure within our organization. This role requires a unique blend of technical expertise in artificial intelligence and strong operational management skills to ensure seamless deployment, monitoring, and optimization of AI solutions across various business units. The ideal candidate will bridge the gap between technical teams and business stakeholders, driving efficiency and innovation through AI-powered operations.
AI Operations Manager responsibilities include:
1. Oversee the end-to-end lifecycle management of AI systems and ML models in production environments
2. Develop and implement AI operational strategies, policies, and best practices for scalable deployment
3. Monitor AI system performance, model drift, and operational metrics using tools like Datadog, Prometheus, or custom dashboards
4. Lead incident response and resolution for AI system failures or performance degradation
5. Manage AI infrastructure costs and optimize resource allocation across cloud platforms (AWS, GCP, Azure)
6. Coordinate between data science, engineering, and business teams to ensure alignment on AI operational priorities
7. Implement and maintain MLOps practices for continuous integration and deployment of machine learning models
8. Establish SLAs and performance benchmarks for AI systems and ensure compliance with operational standards
1. Bachelor's degree in Computer Science, Data Science, or related technical field
2. 5+ years of experience in AI/ML operations, DevOps, or infrastructure management
3. Proven experience with cloud AI services (AWS SageMaker, Google Vertex AI, Azure ML)
4. Strong understanding of MLOps principles and tools (MLflow, Kubeflow, TFX)
5. Experience with containerization technologies (Docker, Kubernetes) and orchestration platforms
6. Proficiency in Python and experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn)
7. Demonstrated ability to manage and scale AI systems in production environments
8. Excellent problem-solving skills and experience with incident management processes
Preferred Qualifications
1. Master's degree in AI, Machine Learning, or related quantitative field
2. Experience with real-time inference systems and high-throughput AI applications
3. Background in managing AI operations for enterprise-scale applications
4. Knowledge of AI ethics, fairness, and responsible AI practices
5. Experience with automated testing and validation of machine learning models
6. Previous work in regulated industries with AI compliance requirements
7. Certifications in cloud AI technologies (AWS Machine Learning Specialty, Google Cloud ML Engineer)
8. Experience with cost optimization strategies for AI infrastructure
Bonus Skills
1. PhD in Machine Learning, Artificial Intelligence, or related field
2. Publications or contributions to AI/ML open-source projects
3. Experience with edge AI deployment and IoT integration
4. Knowledge of generative AI operations and large language model management
5. Background in AI security and adversarial attack prevention
6. Experience with multi-cloud AI strategy implementation
7. Proven track record of reducing AI operational costs by 20%+
8. Leadership experience in AI incident response teams
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