MLOps Engineer
Job Summary
To enhance machine learning capabilities, the full-time remote MLOps Engineer will design and manage ML infrastructure, build scalable pipelines, and optimize compute and cost for production systems.
Key responsibilities
- Design and operate ML infrastructure for high-throughput model workflows
- Build reproducible training and evaluation pipelines with versioning and artifact tracking
- Optimize GPU and CPU workloads while ensuring reliability and observability of ML systems
Required 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
- Strong Python skills and familiarity with infrastructure as code and automation
- Proven ability to ship and operate production ML systems with SLOs
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