Ml Infrastructure Engineer

Later

Vancouver, Canada
On-site
Mlops
Ci/cd pipelines
Docker
Own the systems that support model experimentation, training, deployment, and monitoring at scale

Job Summary

  • Own the systems that support model experimentation, training, deployment, and monitoring at scale.
  • Design, build, and maintain production-grade model deployment and inference systems using CI/CD pipelines, containerized services (Docker), and API frameworks (e.g., Flask).
  • Partner closely with Data Scientists, Analysts, Platform Engineers, and Product Engineers to support end-to-end ML workflows.

Matching Summary

Own the systems that support model experimentation, training, deployment, and monitoring at scale.

Skills & Requirements

Must-have

  • MLOps
  • CI/CD pipelines
  • Docker
  • Flask-based APIs
  • AWS and/or GCP
  • Python programming

Nice-to-have

  • LLMs or generative AI
  • feature stores
  • real-time inference systems
  • ML governance frameworks
  • Go, Java, or Scala

Key Requirements

  • 4+ years of experience
  • Experience with ML lifecycle tooling
  • Experience managing container orchestration platforms
  • Experience managing GPU-based workloads
  • Experience with infrastructure-as-code tools

Work Rights

Not specified

Tailored Resume

Cover Letter