Senior Engineer, ML Operations

What you'll do

• Own the end-to-end ML and LLM lifecycle including experiment tracking, model registries, versioning, and lineage.
• Design and operate model serving for batch and low-latency online inference with autoscaling and GPU optimization.
• Productionize agentic workflows and automations using orchestration frameworks and containerized, sandboxed execution.
• Implement CI/CD pipelines, automated testing, and observability for ML models and agentic systems.
• Monitor model quality, drift, latency, token usage, and cost using tracing and automated alerting tools.
• Drive compute and cost efficiency through GPU scheduling, quota management, and LLM provider routing.
• Build self-service ML and agent tooling to provide reusable templates and "golden paths" for other teams.
• Establish MLOps and LLMOps standards and best practices across the organization.

What we're looking for

• Degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
• 4+ years of software, ML, or infrastructure engineering experience.
• Hands-on experience with MLOps/LLMOps and a track record of taking ML models or AI agents into production at scale.
• Strong experience with ML and LLM lifecycle tooling including experiment tracking, evaluation, observability, and model registries.
• Strong experience with containerization and orchestration (Docker, Kubernetes) and scaling GPU and inference workloads.
• Experience with a major cloud platform (Azure preferred) and its AI services using IaC and CI/CD for ML.
• Proficiency in Python and familiarity with Bash for automation, tooling, and pipeline development.
• Ability to travel up to 10%.