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Arlington, VA
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Job Description
Role: DevOps / DataOps Engineer (Private AI / AI Workbench)
Location: Washington, DC (Onsite / Hybrid)
Duration: Long-term contract
Job Description:
The DevOps/DataOps Engineer builds and operates the deployment and data foundations for AI Workbench. This role automates infrastructure provisioning, manages Kubernetes/container operations, builds CI/CD pipelines, and implements secure data pipelines and observability so AI-driven solutions run reliably in private or hybrid environments.
Key Responsibilities
• Provision and manage environments using Infrastructure as Code (IaC) for compute, networking, storage, and security controls.
• Administer Kubernetes/container platforms to host AI Workbench services, agent runtimes, model endpoints, and supporting components.
• Build and maintain CI/CD pipelines for application/services, agent configurations, and infrastructure updates with automated checks.
• Implement DataOps pipelines for RAG ingestion: secure connectors, preprocessing jobs, scheduling, data quality checks, and lineage tracking.
• Implement observability: logs, metrics, traces, dashboards, alerting, and SLO/SLI monitoring across platform and workloads.
• Harden environments: secrets management, vulnerability scanning, image signing, policy-as-code, and least-privilege access.
• Support release management, incident response, and operational handover including runbooks and knowledge transfer.
• Optimize performance and cost: resource sizing, autoscaling policies, GPU scheduling, and storage optimization.
Required Qualifications
• 5+ years in DevOps/SRE and/or DataOps roles supporting enterprise platforms.
• Hands-on experience with Kubernetes and container tooling.
• Experience building CI/CD pipelines and IaC automation.
• Strong knowledge of security practices in platform operations.
• Experience with data pipelines and ETL/ELT concepts.
Preferred Qualifications
• Experience supporting AI/ML platforms, GPU workloads, or model inference services.
• Experience with policy-as-code (OPA/Gatekeeper-like concepts) and compliance-driven operations.
• Familiarity with vector databases and search indexing operations.
• Experience with hybrid connectivity and network security patterns.
Key Skills
• Kubernetes, containers, Helm/GitOps concepts
• IaC (Terraform/Ansible), CI/CD
• Data pipelines, scheduling, data quality
• Security hardening, secrets, scanning
• Monitoring/observability (metrics, logs, tracing)
Location: Washington, DC (Onsite / Hybrid)
Duration: Long-term contract
Job Description:
The DevOps/DataOps Engineer builds and operates the deployment and data foundations for AI Workbench. This role automates infrastructure provisioning, manages Kubernetes/container operations, builds CI/CD pipelines, and implements secure data pipelines and observability so AI-driven solutions run reliably in private or hybrid environments.
Key Responsibilities
• Provision and manage environments using Infrastructure as Code (IaC) for compute, networking, storage, and security controls.
• Administer Kubernetes/container platforms to host AI Workbench services, agent runtimes, model endpoints, and supporting components.
• Build and maintain CI/CD pipelines for application/services, agent configurations, and infrastructure updates with automated checks.
• Implement DataOps pipelines for RAG ingestion: secure connectors, preprocessing jobs, scheduling, data quality checks, and lineage tracking.
• Implement observability: logs, metrics, traces, dashboards, alerting, and SLO/SLI monitoring across platform and workloads.
• Harden environments: secrets management, vulnerability scanning, image signing, policy-as-code, and least-privilege access.
• Support release management, incident response, and operational handover including runbooks and knowledge transfer.
• Optimize performance and cost: resource sizing, autoscaling policies, GPU scheduling, and storage optimization.
Required Qualifications
• 5+ years in DevOps/SRE and/or DataOps roles supporting enterprise platforms.
• Hands-on experience with Kubernetes and container tooling.
• Experience building CI/CD pipelines and IaC automation.
• Strong knowledge of security practices in platform operations.
• Experience with data pipelines and ETL/ELT concepts.
Preferred Qualifications
• Experience supporting AI/ML platforms, GPU workloads, or model inference services.
• Experience with policy-as-code (OPA/Gatekeeper-like concepts) and compliance-driven operations.
• Familiarity with vector databases and search indexing operations.
• Experience with hybrid connectivity and network security patterns.
Key Skills
• Kubernetes, containers, Helm/GitOps concepts
• IaC (Terraform/Ansible), CI/CD
• Data pipelines, scheduling, data quality
• Security hardening, secrets, scanning
• Monitoring/observability (metrics, logs, tracing)
group id: 91027337