MLOps Platform Engineer

MAASco Tech LLC is seeking an experienced MLOps Platform Engineer to design, build, and support enterprise-grade machine learning operations capabilities. This hands-on engineering role involves managing MLOps platform components, overseeing model deployment, and collaborating with data teams to operationalize machine learning solutions.

Responsibilities:

• 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
• 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
• 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
• 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
• 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

Requirements:

• 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
• Experience managing Data Analytics Platforms / Tools (e.g., Domino, SageMaker)
• Experience with ML lifecycle tools such as MLflow, or similar
• Experience supporting GPU-based workloads or distributed training environments
• Familiarity with enterprise MLOps architectures and patterns (batch, real-time, microservices)
• Understanding of data processing frameworks and feature pipelines