Ai/ML Ops Engineer

Role Purpose

Build and operate the platform capabilities required to deploy, monitor, govern and continuously improve ML and GenAI solutions.

Key Responsibilities

• Build CI/CD and continuous-training pipelines for models, prompts and AI services.
• Automate packaging, deployment, approval, rollback, retirement and artifact versioning.
• Manage model registries, feature pipelines, environments and repositories.
• Monitor model performance, drift, latency, reliability, cost and data quality.
• Provision cloud and Kubernetes infrastructure through Infrastructure as Code and maintain runbooks and audit trails.

Required Skills

• Azure ML, MLflow and Databricks
• Azure DevOps, GitHub Actions or Jenkins
• Docker, Kubernetes, Terraform and Python
• Model registry, feature store and secrets management
• Observability, release governance and incident support

Qualifications

Bachelor's degree in Computer Science, Engineering or related field. Azure, Kubernetes or DevOps certification preferred.

Key Deliverables / Success Measures

• Clear, approved deliverables aligned to squad objectives
• Evidence of quality, security, compliance and governance controls
• Predictable collaboration across business and technology stakeholders
• Measurable improvement in product adoption, delivery flow or operational outcomes