Lead Platform Engineer

Job title: MLOps Platform Engineer

Location: Reston VA - In person interviews so need Local In EAST coast only

The Data Modeling Analytics & AI Engineering team is seeking an experienced MLOps Platform Engineer to design, build, and support enterprise-grade machine learning operations capabilities. This role will play a key part in enabling scalable, reliable, and secure ML model development and deployment across our cloud and container platforms.

This is a hands-on engineering role requiring strong expertise in AWS, Kubernetes (EKS), CI/CD automation, containerization, and ML platform operations. The ideal candidate will have solid engineering fundamentals combined with practical knowledge of ML workflows, deployment patterns, and platform reliability.

Key Responsibilities

• Platform Engineering & Operations Engineer, manage, and support MLOps platform components across AWS and EKS-based environments.
• Oversee deployment, configuration, and operation of infrastructure used for ML training, batch inference, and real-time model serving.
• Ensure platform availability, resilience, and performance across dev, test, and production environments.
• Implement role-based access controls (RBAC), network policies, and scalable namespace designs within EKS.
• Model Deployment & CI/CD Automation Build and support CI/CD pipelines (GitLab) for model packaging, container image builds, vulnerability scanning, and automated deployment flows.
• Enable standardized model release processes including environment promotion, versioning, and rollback workflows.
• Integrate CI/CD with ML frameworks, model repositories, artifacts, and runtime environments.
• Container & Kubernetes Workloads Design and manage EKS workloads supporting containerized ML jobs and microservices.
• Implement auto-scaling, resource quotas, cluster optimization, and multi-tenant workload isolation.
• Support GPU and CPU-based training/inference workloads.
• Monitoring, Observability & Optimization Implement logging, monitoring, and alerting for ML pipelines, model endpoints, batch jobs, and platform components.
• Analyze compute, storage, and data transfer usage to optimize cost efficiency across ML workloads.
• Perform incident response, root cause analysis, and long-term remediation planning.
• Collaboration & Enablement Partner with Data Scientists, ML Engineers, and application teams to operationalize end-to-end machine learning solutions.
• Provide technical guidance on best practices for ML model lifecycle management, deployment patterns, and scalable architectures.
• Contribute to documentation, runbooks, onboarding materials, and internal knowledge bases.

Required Qualifications

• 3+ years of hands-on experience with AWS services, including EKS, EC2, S3, IAM, CloudWatch, and ECR.
• Strong experience operating and troubleshooting Kubernetes (preferably AWS EKS).
• Proficiency in containerization (Docker) and orchestration concepts.
• Strong programming/scripting experience in Python and Bash.
• Experience building and managing CI/CD pipelines (GitLab or equivalent).
• Familiarity with machine learning workflows, including training, inference, and model monitoring.
• Experience with infrastructure-as-code (Terraform or CloudFormation).
• Experience supporting production platforms, including incident management and root cause analysis.

For applications and inquiries, contact: hirings@openkyber.com