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Strong employer brand, metro location, mid-level generalist DevOps title, and broad toolset requirements increase applicant competition.
Core DevOps skills are transferable but the MLOps focus raises domain specificity, so industry transferability is moderate.
Explicit 2–5 years requirement plus mandatory specific tools (Kubernetes, cloud, Terraform, CI/CD) makes shortlisting strict.
Build and maintain CI/CD pipelines and infrastructure to deploy AI/ML applications reliably and securely at scale.
Support ML model deployment lifecycle including versioning, retraining, rollback, and containerization using Docker/Kubernetes/OpenShift.
Implement pipeline orchestration, monitoring, security scans, and manage hybrid cloud/on-prem infrastructure for AI/ML workloads.
2–5 years of DevOps/SRE experience.
Proficiency in Python scripting, Kubernetes, Docker, and one major cloud platform.
Bachelor’s degree or equivalent experience is mandatory.
Experience with Git-based version control and CI/CD tools (Jenkins, Tekton, Harness, GitHub Actions).
Intermediate level Applications Development Programmer Analyst with DevOps focus on AI/ML deployment pipelines.
Able to work with growing autonomy and provide coaching to junior colleagues, applying sound judgment on risk, compliance, and reputational matters.
Experienced in managing hybrid cloud/on-prem infrastructure and enforcing security and compliance standards in ML applications.