MLOps Engineer

Phase: Initial phase setup, scaling with later phases

Required experience: 6+ years in ML / DevOps, with 3+ years dedicated MLOps setup and operations.

Role summary

Sets up and configures the MLOps foundation so that models can be trained, deployed, monitored, and governed on top of the data platform. Establishes the tooling and pipelines that later phases, including agentic AI, will depend on.

Key responsibilities

• Set up and configure the MLOpsplatform and CI/CD for models.
• Build pipelines for model training, deployment, versioning, and monitoring.
• Integrate MLOps tooling with the lakehouse and feature data.
• Establish model governance, lineage, and audit trails.
• Support reproducibility and environment management across dev and production.
• Prepare the MLOps foundation to support agentic AI capabilities in later phases.

Must-have skills and experience

• Proven MLOps setup and configuration experience end to end.
• Experience with model CI/CD, versioning, and monitoring in production.
• Familiarity with Azure ML or equivalent, and readiness for Snowflake-based workflows.
• Containerization and orchestration experience (Docker, Kubernetes).
• Strong understanding of model governance and reproducibility.

Nice to have

• Feature store experience.
• Exposure to agentic AI or LLM operations.
• Infrastructure-as-code experience.

Relevant stack

Azure ML / MLflow or equivalent, Docker, Kubernetes, CI/CD tooling, integrated with the lakehouse and Snowflake.

General attributes

• Proactive and self-driven, able to take ownership and move work forward without waiting to be told.
• AI-enabled in day-to-day work, comfortable using AI tools and copilots to accelerate delivery and quality.
• Strong self-learner who stays current with evolving tools, platforms, and practices.
• Good team player who collaborates well across engineering, operations, and stakeholder groups.