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Mid-level DevOps/MLOps role in metro with common title, 3–5yr band, and broad skillset.
Specialized MLOps and cloud infrastructure skills make it moderately transferable across industries.
Explicit 3–5 years plus mandatory IaC, cloud, Kubernetes, monitoring and MLOps skills.
Develop, automate, and maintain AWS-based infrastructure and AI/ML model deployment pipelines using IaC and CI/CD tools like Terraform, Ansible, Jenkins, and GitHub workflows.
Implement AI-driven observability, self-healing, and monitoring solutions to enhance system uptime, availability, and proactive incident management using AIOps technologies.
Design and configure disaster recovery solutions on AWS, conduct risk assessments, and achieve key performance indicators including maintaining high system and model availability.
3 to 5 years of experience in DevOps or MLOps practices within large-scale enterprise environments.
Strong proficiency in AWS services and certifications (Certified Solutions Architect or DevOps Engineer preferred).
Experience with Infrastructure as Code tools such as Terraform or AWS CloudFormation, and container orchestration platforms like Kubernetes, ECS, EKS.
Work Experience Required: 3 to 5 years specifically in monitoring and AIOps solutions (e.g., DataDog, Grafana).
Experienced in managing end-to-end AI/ML infrastructure deployments with a focus on automation and scalable cloud solutions using AWS and MLOps technologies.
Comfortable working with AI/ML infrastructure components including AWS SageMaker, vector databases, LLM deployment frameworks, and AI-assisted developer tools.
Capable of independently managing risk, disaster recovery strategies, and continuous operational improvements in a global engineering team environment.