About the role
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).