Senior MLOps Engineer

We’re looking for a Senior MLOps Engineer to help design, build, and scale our next-generation data infrastructure. You’ll work at the intersection of machine learning, cloud engineering, and data operations—developing robust pipelines and workflows that power advanced analytics and production ML systems.

Key Responsibilities:

• Design, build, and maintain scalable data and ML workflow orchestration systems.
• Develop containerized applications and pipelines leveraging Kubernetes for deployment and scaling.
• Partner with data scientists and ML engineers to product ionize end-to-end machine learning solutions.
• Implement best practices for CI/CD, observability, and automated testing in data and ML environments.
• Optimize performance, cost, and reliability of workloads in Azure or Google Cloud (GCP).
• Contribute to infrastructure-as-code (IaC) initiatives and cloud automation for data platform services.

Qualifications:

• 5+ years of experience in data engineering, platform engineering, or related roles.
• Strong hands-on experience writing ML workflows using tools such as Kubeflow, Airflow, Argo, or MLflow.
• Proficiency managing and deploying workloads on Kubernetes (e.g., Helm, Operators, custom resources).
• Deep understanding of Azure Data Services (Data Factory, Databricks, Synapse) or GCP equivalents (Vertex AI, Dataflow, Big Query).
• Solid background in Python or Go for data pipeline and automation development.
• Practical knowledge of IaC tools (Terraform, Pulumi) and container technologies (Docker).
• Familiarity with modern data architectures (lakehouse, streaming, feature stores) is a plus.

Bonus Skills:

• Experience with workflow observability and lineage tools (e.g., Great Expectations, Open Lineage).
• Knowledge of distributed systems performance optimization.
• Contributions to open-source data or ML projects.