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MLOps Engineer

  • Closed
  • US Company | Medium (51-250 employees)
  • LATAM (100% remote)
  • 5+ years
  • Long-term (40h)
  • Advertising Services
  • Full Remote
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Required skills

  • AWS
  • ML Infrastructure
  • Python
  • Terraform
  • CI/CD
  • Docker

Requirements

Must-haves

  • 5+ years of DevOps, MLOps, or Cloud Infrastructure Engineering experience
  • Experience with AWS services (CDK, Lambda, EC2, S3, SageMaker, CloudWatch)
  • Proficiency with Infrastructure as Code (IaC) tools (Terraform, CloudFormation)
  • Strong experience with Python for scripting and automation
  • Proficiency with containerization using Docker
  • Experience building and maintaining CI/CD pipelines for ML workflows
  • Deep knowledge of ML model lifecycle management, including deployment, monitoring, and retraining
  • Based in Brazil, Argentina, Paraguay, Colombia, or Mexico
  • Strong communication skills in both spoken and written English

Nice-to-haves

  • Startup experience
  • AWS Certifications (e.g. DevOps Engineer, Solutions Architect, Machine Learning Specialty)
  • Background in software engineering or ML/AI infrastructure
  • Bachelor’s Degree in Computer Engineering, Computer Science, or equivalent

What you will work on

  • ML Infrastructure Architecture & Automation
  • Design, provision, and manage AWS infrastructure for ML workloads using AWS CDK and CloudFormation
  • Architect secure, scalable, and cost-efficient ML environments for experimentation, training, and inference
  • Implement cloud-native services (e.g. EC2, ECS, Lambda, S3, RDS, SageMaker, Bedrock, Step Functions)
  • Apply best practices for security, compliance, and disaster recovery in ML infrastructure
  • Model Deployment & CI/CD
  • Design and maintain CI/CD pipelines for training, deployment, and retraining of models using CodePipeline, CodeBuild, GitHub Actions, or similar
  • Automate testing, versioning, and rollback strategies for applications and ML models
  • Build and manage Docker containers for microservices and ML applications
  • MLOps Enablement
  • Collaborate with ML engineers to deploy, monitor, and maintain models in SageMaker
  • Develop end-to-end pipelines for data preprocessing, feature engineering, training, inference, and retraining
  • Integrate model monitoring, drift detection, and automated retraining triggers
  • Monitoring, Observability & Performance
  • Implement observability frameworks for ML workloads using CloudWatch, DataDog, and other tools
  • Track inference latency, accuracy, and resource usage to optimize performance
  • Troubleshoot production ML systems and lead incident resolution
  • Collaboration & Documentation
  • Partner with software, ML, and data teams to promote MLOps best practices
  • Maintain clear documentation for infrastructure, deployments, and operational processes
  • Contribute to code reviews and architectural discussions
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