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

Requirements

• 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

Qualifications

• Experience with CI/CD processes and automation
• Experience with Infrastructure as Code tools such as Terraform, Bicep
• Proficiency in containerized application deployments and container orchestration – experience with Kubernetes, especially AKS would be a huge plus
• Experience standing up and managing observability tools such as Datadog, Azure Monitor or Grafana for APM, LLM Ops and model performance monitoring
• Experience deploying production-ready machine learning models
• Experience with Model explainability (SHAP, LIME) or similar
• Experience with cloud cost management and practices (e.g., Azure Cost Management, chargeback/show back models)

Skills

• Experience in Azure, particularly AKS, ACR, ARM, App Service, Azure Machine Learning and AI
• Familiarity with semantic search, retrieval-augmented generation (RAG), or embedding pipelines
• Exposure to managing and monitoring ML workloads that support generative AI or advanced analytics use cases
• Proficiency with Snowflake
• Experience with workflow orchestration platforms such as Apache Airflow, Argo Workflow, Prefect, etc.