AI ML DevOps Engineer

Position: AI ML DevOps Engineer

Location: Boston MA-Remote

Duration: 12 months

Roles & Responsibilities

• Design implement and maintain ML pipelines for training testing and deploying AI/ML models.
• Manage and optimize cloud-based ML infrastructure (GCP Vertex AI AWS SageMaker or equivalent).
• Build and maintain CI/CD pipelines for ML and AI-driven applications.
• Monitor troubleshoot and optimize model performance and system reliability.
• Automate workflows for data ingestion training deployment and monitoring.
  • Collaborate with cross-functional teams to ensure scalable secure and compliant ML operations.
• Apply MLOps best practices for reproducibility versioning and governance of ML models.

Must-Have

• 5 years of experience in DevOps CloudOps or MLOps.
• 5 years of hands-on experience with GCP AI/ML services (Vertex AI AI Platform BigQuery ML) or AWS ML services (SageMaker etc.).
• Strong background in containerization and orchestration (Docker Kubernetes).
• Proficiency with infrastructure-as-code tools (Terraform CloudFormation Deployment Manager).
• Experience building/maintaining CI/CD pipelines (Jenkins GitHub Actions GitLab CI ArgoCD).
• Strong programming skills in Python Bash or Go with experience in ML frameworks (TensorFlow PyTorch Scikit-learn).

Nice-to-Have

• Prior experience working directly with data scientists/ML engineers on production ML deployments.
• Familiarity with ML model governance compliance and monitoring at scale.
• Certifications such as:
  • Google Cloud Professional Machine Learning Engineer
  • Google Cloud Professional Data Engineer
  • AWS Certified Machine Learning Specialty
  • Certified Kubernetes Administrator (CKA)
  • Google Professional Cloud Architect

Thanks

Naveen Samilla

Momento USA Exceeding Customer Expectations

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