MLOps Lead
Own model deployment and observability blueprints.
What You'll Do
Own model deployment, observability, and automation blueprints that keep AI workloads resilient in production.
Your Impact
Here's what you'll be responsible for in this role
Design CI/CD templates for data pipelines, model training, and inference
Define infrastructure reference stacks spanning containers, GPUs, and edge
Implement monitoring for drift, bias, data freshness, and SLA adherence
Collaborate with SRE and security on access control and compliance
Document runbooks, release gates, and rollback mechanisms
What We're Looking For
- 7+ years in DevOps/SRE with 4+ years focusing on MLOps
- Deep knowledge of Kubernetes, Argo, Terraform, and observability suites
- Experience orchestrating training pipelines on managed and self-hosted GPUs
- Familiarity with model registries, feature stores, and experiment tracking
- Strong scripting skills (Python, Go, or Bash) and automation mindset
What We Offer
We believe in taking care of our team members
Apply for MLOps Lead
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MLOps Lead
Platform Engineering
Key Skills
Application Tips
- Keep your cover letter under 600 words
- Highlight measurable achievements
- Tailor skills to match the job description
- Upload a PDF resume for best results
What happens next?
- 1Application review (3–5 days)
- 2Technical / culture screening call
- 3Final interview round
- 4Offer & onboarding