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Mid-level specialized MLOps/DevOps role with common cloud skills, moderate competition.
Core platform and MLOps skills transfer across industries but require AI-platform specialization, so moderate sensitivity.
Explicit 3+ years plus mandatory Terraform/Kubernetes/GCP and MLOps/production experience increases filter strictness.
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Build and operate the core AI platform on cloud infrastructure including compute, networking, runtime environment, and ensure it is secure, production-grade, and reliable.
Develop, provision, and manage infrastructure as code (e.g., Terraform) and maintain CI/CD pipelines with automated builds, tests, and security gates for AI solution deployment.
Collaborate with AI Automation Engineers, architects, and business teams to provide reusable components, security guardrails, observability, and enable rapid, safe AI solution delivery.
Bachelor's degree or equivalent in Computer Science, Engineering, or related field.
Minimum 3 years experience in platform, DevOps, infrastructure, or MLOps engineering with hands-on production cloud system operation.
Strong expertise in cloud platforms (Google Cloud preferred), infrastructure as code (Terraform), containerization/orchestration (Docker, Kubernetes), and CI/CD engineering.
Experience running ML/LLM/data-intensive systems in production (MLOps/LLMOps), plus solid security engineering fundamentals (IAM, secrets, policy-as-code) and observability practices.
Experienced platform or MLOps engineer focused on building and maintaining secure, scalable AI/ML infrastructure in cloud environments, especially Google Cloud.
Comfortable working collaboratively across teams including automation engineers, architects, InfoSec to deliver a production-grade platform with strong governance and reliability focus.
Has hands-on knowledge with model serving, versioning, runtime for LLM/agent-based systems, and delivering user-centric self-service developer platforms or paved roads.