MLOps Engineer

Responsibilities

• Lead hands-on implementation of automation-first DevOps and MLOps practices, enabling infrastructure-as-code and consistent, repeatable environment provisioning
• Design and manage intelligent DataOps pipelines with automated data quality monitoring and anomaly detection
• Standardize observability practices across AI/ML and other development teams including logging, metrics, tracing, and model performance monitoring, ingesting data from multiple platforms
• Design and deploy containerized ML workloads, partnering with Infrastructure Engineering for cluster provisioning and governance
• Extend existing CI/CD pipelines to support automated infrastructure changes and ML workflows
• Implement AI-driven data validation, schema drift detection, and metadata management
• Establish governance frameworks for AI systems, including bias detection, explainability, and auditability
• Extend existing Azure RBAC strategy by automating role and permission management to reduce manual intervention
• Collaborate with Infrastructure Engineering to automate infrastructure provisioning
• Act as a technical point of contact for DevOps and MLOps practices, developing reusable patterns, documentation, and proof-of-concepts to drive adoption

Qualifications

• Bachelor’s degree in Computer Science, Data Science, Information Systems, or a related field
• 5+ years of experience in DevOps, MLOps, Data Engineering, Software Engineering or Site Reliability Engineering
• Strong understanding of cloud infrastructure and experience working with at least one major cloud provider, preferably Azure
• Proficiency in at least one objected-oriented programming language, preferably python with hands-on experience in ml frameworks like TensorFlow, PyTorch or Scikit-learn