Cloud MLOps Administrator

Job Title : Cloud MLOps Administrator

Location : Onsite New Jersey

Duration : 12+ Months

Experience : 10+ Years

Certification : Cloud certification required (AWS preferred)

Overview

We are seeking a highly skilled Cloud MLOps Administrator to manage, modernize, and optimize ML workloads on cloud platforms-primarily AWS. The ideal candidate should have strong experience in deploying, scaling, and maintaining machine learning pipelines, containerized ML workloads, and cloud-native infrastructure. This role involves supporting ML engineers, automating model lifecycles, ensuring security / compliance, and enabling efficient, scalable MLOps practices.

Key Responsibilities ML Platform & Infrastructure Management

Administer and optimize cloud-based ML environments using AWS services (EKS / ECS, EC2, S3, Lambda, CloudWatch, IAM).

Manage containerized ML workloads using EKS / ECS and support orchestration of model training / inference environments.

Configure and maintain ML pipelines for training, validation, and deployment.

MLOps Pipeline Automation

Implement CI / CD pipelines (AWS CodePipeline, Jenkins, GitHub Actions) for ML workflows.

Automate model deployment, rollback, versioning, and monitoring.

Manage Infrastructure as Code (IaC) using Terraform and CloudFormation.

Application Modernization for ML Workloads

Support migration of legacy ML scripts / workloads into modern, cloud-native architectures.

Enable microservices and serverless patterns for scalable model hosting.

Security, Compliance & Governance

Apply best practices for IAM, VPC design, encryption, and network security for ML workloads.

Ensure ML systems align with cloud governance, compliance frameworks, and enterprise policies.

Continuously monitor ML infrastructure using CloudWatch and logging services.

Performance Optimization

Conduct performance tuning for ML model execution environments.

Optimize compute, storage, and data flow to reduce cost and improve efficiency.

Collaboration & Support

Work closely with Data Science, ML Engineering, DevOps, and Cloud Architecture teams.

Support ML engineers with environment setup, model deployments, and issue resolution.

Provide documentation, environment guides, and operational best practices.

Required Technical Skills

Strong expertise in AWS services : EKS / ECS, EC2, S3, Lambda, RDS, DynamoDB, CloudWatch, API Gateway .

Experience building and automating ML pipelines and MLOps workflows .

Hands-on experience with Terraform, CloudFormation , and CI / CD tools.

Strong understanding of containerization (Docker, Kubernetes).

Solid knowledge of IAM, VPC, security groups, encryption standards .

Experience modernizing ML systems and managing cloud scalability.

Preferred Qualifications

AWS Certification (Associate or Professional).

Experience with ML model deployments (SageMaker is a plus).

Knowledge of cloud cost optimization for ML workloads.