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ML OPS Engineer
Duration: 6 Months
Location: Hybrid - San Diego (2-3 hybrid onsite)
Note: This is a W2 only role C2C, C2H will not be considered
Required Experience
The Machine Learning Ops Engineer II designs, builds, and operates scalable machine learning infrastructure and deployment pipelines on AWS. This role partners with data scientists and engineering teams to productionize models, ensuring reliability, security, and cost efficiency across ML systems.
Key Responsibilities
ML OPS Engineer
Duration: 6 Months
Location: Hybrid - San Diego (2-3 hybrid onsite)
Note: This is a W2 only role C2C, C2H will not be considered
Required Experience
The Machine Learning Ops Engineer II designs, builds, and operates scalable machine learning infrastructure and deployment pipelines on AWS. This role partners with data scientists and engineering teams to productionize models, ensuring reliability, security, and cost efficiency across ML systems.
Key Responsibilities
- Design and maintain scalable ML pipelines using AWS (e.g., SageMaker, Lambda, Step Functions, S3)
- Build and manage deployment frameworks for real-time and batch model inference
- Develop Python-based tools for data processing, model packaging, and workflow orchestration
- Implement CI/CD pipelines using GitHub and AWS tooling
- Manage infrastructure using IaC tools (CloudFormation, Terraform, CDK)
- Monitor, log, and troubleshoot ML systems to ensure performance and reliability
- Partner cross-functionally to integrate ML models into applications and data platforms
- Optimize ML infrastructure costs using FinOps best practices
- Improve automation, scalability, and operational efficiency of ML workflows
- Ensure compliance with security, governance, and regulatory standards
- Contribute to architecture decisions, code reviews, and engineering best practices QualificationsRequired
- Bachelor s degree in Computer Science, Engineering, Data Science, or related field (or equivalent experience)
- 3 5 years of experience in software engineering, DevOps, cloud engineering, or MLOps
- Strong Python programming skills
- Hands-on experience with AWS (SageMaker, Lambda, Step Functions, S3, IAM)
- Experience deploying ML models to production and building CI/CD pipelines
- Experience with Infrastructure-as-Code (CloudFormation, Terraform, or CDK)
- Strong troubleshooting skills in cloud or distributed systems
- Experience working with cross-functional teams
- Experience with Docker and container orchestration (ECS or EKS)
- Familiarity with ML observability, feature stores, or model versioning tools
- Knowledge of FinOps or ML cost optimization practices
- AWS certifications (Solutions Architect, DevOps, or ML Specialty)
- Experience in regulated environments or with sensitive data
- Experience building scalable or shared ML platforms