DevOps Engineer with AI/ML Workload
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a DevOps Engineer with AI/ML workload, requiring strong Kubernetes and IaC expertise, multi-cloud identity management, and observability skills. Contract length and pay rate are unspecified. Work is on-site in various locations across the U.S.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
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🗓️ - Date discovered
September 10, 2025
🕒 - Project duration
Unknown
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🏝️ - Location type
On-site
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📄 - Contract type
Unknown
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🔒 - Security clearance
Unknown
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📍 - Location detailed
Austin, Texas Metropolitan Area
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🧠 - Skills detailed
#Observability #ML (Machine Learning) #Kubernetes #Infrastructure as Code (IaC) #SageMaker #AI (Artificial Intelligence) #Scala #Terraform #IAM (Identity and Access Management) #Vault #DevOps #Argo #Cloud #Deployment
Role description
DevOps Engineer with AI/ML workload
Location : Southfield, MI / Princeton, NJ / St. Louis, MO / SFO, Santa Clara, Mountain View CA, Austin, TX and Seattle WA
Responsibilities
Build and manage multi-cloud, AI-ready infrastructure for agent execution.
Implement CI/CD pipelines optimized for LLM workflows, model serving, and agent deployments.
Manage secure identity, RBAC, and secrets for agent interactions.
Automate container orchestration for scalable agent runtime environments.
Requirements
Strong Kubernetes expertise with AI/ML workload orchestration (KServe, Ray, Argo Workflows).
Deep experience with IaC (Terraform, Crossplane, Pulumi) across clouds.
Skilled in multi-cloud identity/IAM integration and secrets management (Vault, SSM).
Familiarity with observability for AI/ML workloads (model telemetry, agent performance dashboards).
Preferred
Familiarity with model serving infrastructure (SageMaker, Bedrock, Vertex AI).
Background in secure, compliant multi-cloud environments.
DevOps Engineer with AI/ML workload
Location : Southfield, MI / Princeton, NJ / St. Louis, MO / SFO, Santa Clara, Mountain View CA, Austin, TX and Seattle WA
Responsibilities
Build and manage multi-cloud, AI-ready infrastructure for agent execution.
Implement CI/CD pipelines optimized for LLM workflows, model serving, and agent deployments.
Manage secure identity, RBAC, and secrets for agent interactions.
Automate container orchestration for scalable agent runtime environments.
Requirements
Strong Kubernetes expertise with AI/ML workload orchestration (KServe, Ray, Argo Workflows).
Deep experience with IaC (Terraform, Crossplane, Pulumi) across clouds.
Skilled in multi-cloud identity/IAM integration and secrets management (Vault, SSM).
Familiarity with observability for AI/ML workloads (model telemetry, agent performance dashboards).
Preferred
Familiarity with model serving infrastructure (SageMaker, Bedrock, Vertex AI).
Background in secure, compliant multi-cloud environments.
