Key responsibilities

• Lead and mentor a team of MLOps engineers, fostering technical growth and a culture of operational excellence

• Define and drive the MLOps roadmap, aligning infrastructure capabilities with Research, Engineering and product objectives

• Establish best practices, standards, and processes for ML infrastructure, deployment, and operations

• Own technical decision-making for ML infrastructure architecture and tooling choices

• Architect and oversee scalable, automated machine learning pipelines, CI/CD workflows, and orchestration frameworks

• Partner with the model-serving team on serving infrastructure strategy (Triton, TorchServe, TensorFlow Serving, KServe), ensuring training-side decisions (checkpoint formats, export pipelines, resource footprint) don't create friction downstream

• Collaborate on inference architecture strategy, bringing training-side context on model size, latency/throughput tradeoffs, and hardware requirements into early research decisions

• Design and maintain feature stores, robust data pipelines, and scalable storage solutions to efficiently handle large volumes of data

• Collaborate with research teams to bridge the gap between experimentation and production

• Define logging, alerting, and monitoring strategy to track model performance, drift, and system reliability

Must have

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field (or equivalent practical experience)

• 7+ years of experience in MLOps, with 5+ years in a technical leadership role

• Strong software engineering skills in Python, with experience in Bash and/or Go

• Proven track record of building and leading high-performing MLOps or infrastructure teams

• Experience building and designing MLOps infrastructure from the ground up

• Deep experience with MLOps platforms (MLflow, WandB, etc.) and frameworks (PyTorch, TensorFlow, etc.)

• Deep experience with model serving frameworks (Triton, TorchServe, TensorFlow Serving, KServe) for high scalability and low latency inference

• Experience building and managing data pipelines to support both model training and inference

• Good experience with Kubernetes on a major cloud provider (AWS, GCP, or Azure) and with infrastructure as code (Terraform, Helm, GitOps)

• Proficient with observability and monitoring tools (Prometheus, Grafana, Datadog, OpenTelemetry)

• Excellent communication skills with ability to translate between research and production contexts

Nice to have

• Experience with workflow orchestration tools (Kubeflow, Airflow, Argo Workflows)

• Experience with FastAPI and backend applications

• Familiarity with data platforms like Databricks or Snowflake

• Experience with LLM/foundation model serving and optimization

• Exposure to SRE practices or cloud security certifications

• Experience scaling ML infrastructure for AI startups

Benefits

• Competitive compensation with salary and equity