MLOps Engineer

Responsibilities:

• Build CI/CD/CT pipelines for automated model training, testing, and deployment.
• Manage ML platform infrastructure (Azure ML, Kubeflow, MLflow, or similar).
• Implement model registry, versioning, and reproducibility practices.
• Monitor model performance, data/model drift, and infrastructure health in production.

Requirements:

• 7+ years of experience in MLOps, DevOps, or ML platform engineering roles.
• Hands-on experience building CI/CD/CT pipelines for automated model training, testing, and deployment.
• Proven experience managing ML platform infrastructure such as Azure ML, Kubeflow, MLflow, or similar tooling.
• Experience implementing model registry, versioning, and reproducibility practices.
• Strong Python skills and familiarity with ML frameworks (e. g., PyTorch, TensorFlow, scikit-learn).
• Solid understanding of containerization and orchestration (Docker, Kubernetes) for ML workloads.
• Experience with cloud infrastructure and infrastructure-as-code (Azure, Terraform, or similar).
• Strong cross-functional communication skills, partnering closely with data science and platform teams.

Good to Have:

• Experience with model monitoring and observability tools (e. g., Evidently, Prometheus, Grafana).
• Familiarity with feature stores and data versioning tools (e. g., Feast, DVC).
• Exposure to LLM/GenAI model serving and fine-tuning pipelines.
• Relevant cloud or Kubernetes certifications (e. g., Azure AI Engineer, CKA).