Machine Learning Operations Specialist | AI Platform Engineer

Employment Type: Full-Time

Level: Mid-Level to Senior

We are seeking a highly skilled Machine Learning Operations Specialist | AI Platform Engineer to build, operate, and optimize scalable platforms for machine learning and artificial intelligence workloads. This role will work closely with Data Scientists, ML Engineers, Software Engineers, Dev Ops, Cloud, and Security teams to establish reliable ML infrastructure, automate model deployment, and support the full machine learning lifecycle from development to production.

• Design, build, and maintain scalable infrastructure for machine learning and AI workloads.
• Develop and operate ML platforms supporting model development, training, deployment, monitoring, and lifecycle management.
• Build automated MLOps pipelines for model training, validation, deployment, and retraining.
• Implement CI/CD and continuous integration practices for machine learning models and applications.
• Manage model versioning, artifact management, experiment tracking, and reproducibility.
• Automate machine learning workflows using appropriate orchestration and pipeline technologies.
• Deploy machine learning models into production environments across cloud, on-premises, or hybrid infrastructure.
• Build scalable model-serving infrastructure capable of supporting high-volume inference workloads.
• Monitor model performance, system health, resource utilization, latency, availability, and reliability.
• Establish monitoring for model drift, data drift, prediction quality, and production performance.
• Develop automated alerts and remediation processes for model and infrastructure issues.
• Collaborate with Data Scientists to product ionize experimental models and machine learning solutions.
• Work with ML Engineers to improve model deployment, scalability, reliability, and operational efficiency.
• Support GPU and high-performance computing infrastructure for AI and machine learning workloads.
• Optimize compute, storage, networking, and cloud resources used by AI platforms.
• Implement Infrastructure as Code to automate the provisioning and management of AI infrastructure.
• Manage containerized machine learning environments using Docker and Kubernetes.
• Build reusable platform components and self-service tools for data science and ML teams.
• Establish standardized environments for model development, testing, staging, and production.
• Implement secure access controls, secrets management, encryption, and infrastructure security practices.
• Support data and model governance requirements throughout the machine learning lifecycle.
• Maintain model deployment standards, operational procedures, runbooks, and technical documentation.
• Troubleshoot model-serving, infrastructure, pipeline, deployment, and performance issues.
• Conduct root-cause analysis and implement long-term solutions for recurring platform problems.
• Support cloud migration, AI platform modernization, and large-scale machine learning initiatives.
• Evaluate new MLOps tools, AI infrastructure technologies, model-serving platforms, and automation frameworks.
• Establish platform reliability and operational metrics for ML and AI services.
• Support disaster recovery, backup, high availability, and business continuity for critical AI platforms.
• Collaborate with cybersecurity teams to ensure AI infrastructure meets organizational security requirements.
• Continuously improve automation, scalability, observability, reliability, and developer experience across the AI platform.

• Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, Data Engineering, or a related field.
• Previous experience in MLOps, Machine Learning Engineering, AI…