Mlops Engineer

MLOps Engineer responsible for operationalizing AI/ML models, including LLM-based solutions, within the Azure ecosystem. This role focuses on building and maintaining end-to-end Machine Learning pipelines, ensuring scalability, reliability, and automation. Key responsibilities include managing infrastructure for model execution, defining standards for reproducibility and observability, automating ML lifecycle steps, and implementing AIOps practices for production monitoring. The role also involves managing the operational lifecycle of generative models, including fine-tuning, evaluation, and RAG pipelines, while ensuring compliance with internal policies and regulatory standards.

What you'd actually do

  1. Design, implement, and maintain end-to-end Machine Learning pipelines (data ingestion, training, validation, deployment, and monitoring).
  2. Operationalize AI/ML models, including LLM-based solutions, within the Azure ecosystem and corporate platforms. Azure cloud experience mandatory.
  3. Apply best practices for version control, CI/CD, automated testing, and model lifecycle governance.
  4. Manage infrastructure for model execution (containers, APIs, microservices, orchestration, autoscaling).
  5. Define and enforce standards for reproducibility, traceability, and model observability.

Skills

Required

  • Python
  • Git
  • CI/CD
  • Docker
  • model orchestration platforms (Azure ML, Kubernetes, ACI, AKS)
  • cloud-based pipelines (preferably Azure)
  • SQL
  • data lakes
  • data warehouses
  • Scikit-learn
  • PyTorch
  • TensorFlow
  • Agile/Scrum methodologies
  • Azure DevOps or GitHub
  • monitoring
  • logging
  • observability
  • system performance

Nice to have

  • Grafana
  • Prometheus
  • App Insights
  • Kibana
  • MLflow Monitoring
  • feature stores
  • model registries
  • large-scale ML architectures
  • integrating APIs
  • microservices
  • serverless architectures
  • analytical thinking
  • communication skills
  • business acumen
  • work with multidisciplinary teams

What the JD emphasized

  • Azure cloud experience mandatory

Other signals

  • MLOps Engineer
  • operationalize AI/ML models
  • end-to-end Machine Learning pipelines
  • model lifecycle governance
  • LLM-based solutions