MLOps and DevOps Engineer

Summary

Join Hakkoda, an IBM Company, as a DevOps/MLOps Engineer and contribute to high-impact projects supporting both traditional DevOps workflows and modern MLOps pipelines in AWS. This role involves building and maintaining CI/CD pipelines, managing AWS infrastructure using Terraform or CloudFormation, collaborating with ML engineers and data scientists, and ensuring secure, scalable, and cost-efficient cloud infrastructure. You will work with Fortune 1000 clients, leveraging cutting-edge AWS services. This is a unique opportunity to help data science teams seamlessly move models into production. Hakkoda offers a collaborative, fast-paced environment with opportunities for growth and learning. The company values diversity and inclusion and encourages applications from all backgrounds.

Requirements

• 3–5+ years of hands-on experience in DevOps or infrastructure engineering, with focus on AWS
• Proficiency with infrastructure-as-code tools like Terraform
• Strong experience with CI/CD pipelines, containerization (Docker), and deployment automation
• Solid knowledge of Amazon SageMaker components including Pipelines, Model Registry, and Studio
• Experience working with cloud storage (S3), serverless tools (Lambda, EventBridge), and IAM roles/policies
• Scripting skills in Python, Bash, or similar languages
• Excellent collaboration skills with cross-functional teams

Responsibilities

• Build and maintain CI/CD pipelines for application and ML model deployment using GitHub Actions or similar tools
• Develop and manage AWS infrastructure using Terraform or CloudFormation
• Collaborate with ML engineers and data scientists to operationalize models via SageMaker Pipelines and SageMaker Studio
• Set up and automate monitoring, alerting, and model drift detection systems
• Manage containerized workloads using Docker; optionally deploy to ECS, EKS, or SageMaker endpoints
• Support model versioning, rollback strategies, and retraining automation
• Ensure secure, scalable, and cost-efficient cloud infrastructure aligned with project requirements
• Provide operational support and documentation for deployment processes

Preferred Qualifications

• Familiarity with ML monitoring, automated retraining, and model drift mitigation
• Experience using MLflow, Feature Stores, or Vertex AI
• Exposure to Kubernetes, especially EKS
• Background in data science workflows or experience working closely with data science teams
• Knowledge of cost optimization and cloud security best practices

Benefits

• Comprehensive Life Insurance: Including dental and vision, wellness, home spa treatments, express doctor visits etc
• Paid Parental Leave
• Flexible PTO Options
• Company Bonus Program
• Asociación Solidarista
• Technical Training & Certifications
• Extensive Learning and Development Opportunities
• Flexible Work-from-Home Policy
• Work from Anywhere Benefit