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Protocol Intelligence
Data-driven signals on your job's competitivenessMid-level experience, metro location, and broad AI-ops skillset drive high candidate competition.
Requires specific AI/MLOps and GCP platform experience so industry transferability is moderately constrained.
Explicit 3–5 years requirement plus mandatory cloud, Kubernetes, Terraform, and MLOps experience makes shortlisting strict.
Job Description
Structured overview of role & requirementsAbout This Role
Design, automate, and operate end-to-end cloud and AI infrastructure supporting AI/ML production environments.
Manage CI/CD pipelines, containerized microservices, observability, and governance to ensure reliability, security, and scale of AI applications.
Collaborate with AI and software engineers to enable development through self-service tooling and automated workflows.
Minimum Requirements
3-5 years of experience in cloud engineering, DevOps, or SRE; 1-2 years specifically with AI/ML/Generative AI production deployments.
Bachelor's or Master's degree in Computer Science, Software Engineering, Cloud Computing, Data Engineering, or related field.
Hands-on experience with cloud infrastructure management (Terraform, Cloud Build, Kubernetes), containerization (Docker/Kubernetes), and CI/CD tooling.
Expertise with a leading cloud provider (e.g., Google Cloud Platform) and cloud security best practices.
Ideal Candidate Profile
Experienced in operating multi-service AI infrastructure at scale with robust reliability and observability practices.
Skilled in integrating AI/ML pipelines including model deployment, version control, monitoring performance, and mitigating AI output issues like hallucination.
Familiar with full-stack AI application architecture and enabling developer productivity through automation and tooling.