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Protocol Intelligence
Data-driven signals on your job's competitivenessMid-level DevOps/MLOps role in a metro with generalist cloud, CI/CD, and Kubernetes requirements.
DevOps skills transfer across industries, though AI/MLOps specialization raises domain specificity moderately.
Explicit 3–5 years plus mandatory Terraform, AWS, Kubernetes, and MLOps tooling makes screening strict.
Job Description
Structured overview of role & requirementsAbout This Role
Develop, automate, and maintain AI/ML model deployments and AWS cloud infrastructure using Infrastructure as Code and automation tools.
Enhance system availability and incident management through AI-driven observability, self-healing solutions, and effective monitoring (CI/CD, MLOps pipelines).
Design disaster recovery strategies on AWS and meet operational goals including KPIs and SLAs for system uptime and model availability.
Minimum Requirements
3 to 5 years experience in DevOps/MLOps or similar role within a large-scale enterprise environment.
Strong proficiency in AWS services, Infrastructure as Code tools (Terraform, CloudFormation), container technologies (Docker, Kubernetes, EKS/ECS).
Experience with monitoring/AIOps tools (DataDog, Grafana, Nagios, New Relic) and automation scripting (Python, Shell, PowerShell).
Work Experience Required: 3 to 5 years; AWS certifications preferred but not mandatory.
Ideal Candidate Profile
Experienced in building scalable AI/ML infrastructure and managing AI workflows using AWS SageMaker and LLM deployment frameworks.
Demonstrates operational focus through responsibility for end-to-end deployment, automation, and proactive monitoring using AIOps and Infrastructure as Code.
Familiar with advanced AI concepts like Agentic AI, RAG, MCP, and practical use of AI-assisted developer tools to automate infrastructure workflows.