MLOps / ML Platform Engineer

Responsibilities

• Design and operate ML infrastructure: Manage data, training, serving, and inference systems for high-throughput model workflows.
• Build scalable pipelines: Implement reproducible training and evaluation pipelines with versioning, scheduling, and artifact tracking.
• Optimize compute and cost: Tune GPU and CPU workloads, manage clusters, and drive efficiency via rightsizing, spot scheduling, and caching.
• Serve models in production: Operate APIs for low-latency inference with autoscaling, blue-green or canary rollouts, and rollback safety.
• Ensure reliability and observability: Define and own SLOs; instrument pipelines and services to track latency, cost, drift, and data quality.
• Secure and automate: Manage IAM, secrets, and container security; automate deployment pipelines via CI/CD and infrastructure as code.
• Collaborate cross-functionally: Partner with research scientists and AI engineers to deliver models from experiment to production with minimal friction.
• Document and enable: Build templates, runbooks, and internal tooling that make ML workflows repeatable, safe, and fast.

Qualifications

• 4+ years of experience in ML platform, DevOps, or infrastructure engineering.
• Deep knowledge of Kubernetes, CI/CD, containers, and cloud infrastructure (AWS, GCP, or Azure).
• Hands-on experience managing GPU clusters and training/inference pipelines.
• Familiarity with data orchestration and storage formats (Delta, Parquet, Polars, Spark).
• Proven ability to ship and operate production ML systems with SLOs.
• Strong Python skills and comfort with infrastructure as code and automation.
• Experience with observability and cost optimization at scale.

Nice to Have