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MLOps Engineer AWS
Worldwide | Sept. 1, 2026
Company: 2BRAINS
Country: Worldwide
Type: Remote
Employment: Full-time
Description: We integrate strategy, design, and technology to empower companies and tech disruptors. With a team of more than 250 professionals, we stand out as leaders in digital innovation, creating adaptable technology platforms and digital products
2Brains is a technology consulting firm that drives the growth and evolution of leading organizations in Latin America. We design and build our clients’ digital future through a deep and strategic integration of business, experience, and technology, turning complex challenges into real impact.
Today, 2Brains is part of Acid Labs, one of the region’s leading tech and innovation consulting groups. This acquisition strengthens our value proposition by expanding our scale, technological capabilities, and regional presence, and enables us to support our clients with more robust, agile, and high-impact solutions throughout their transformation journey.
Education: Degree in Computer Engineering, Computer Science, Systems Engineering, or related fields. Official AWS certifications (AWS Certified DevOps Engineer / AWS Certified Machine Learning Specialty) are preferred. Experience: At least 3 to 5 years of demonstrable experience in DevOps, MLOps, or Cloud Data Architect roles, actively participating in the deployment of machine learning and AI models and the automation of cloud infrastructure. Practical experience designing and integrating architectures that consume APIs, large-scale databases, or data lakes under standardized contracts.
Required Foundational Knowledge
AWS and Infrastructure as Code: Solid knowledge of the AWS data and AI ecosystem (e.g., SageMaker, EKS, ECS, S3, Lambda, AWS Glue), along with provisioning tools such as Terraform or CloudFormation. CI/CD Control and Deployment Systems: Advanced proficiency in Git and Git platforms (GitHub Actions, GitLab CI, AWS CodePipeline) to design workflows focused on the automated deployment of microservices and models Contracts and Data Architecture: Understanding of data schemas (JSON Schema, Avro, Protobuf), database architectures (SQL/NoSQL), data orchestration tools (Airflow), and streaming/batch flows. (Preferred) Programming and ML Frameworks: Intermediate/advanced proficiency in Python and familiarity with ML libraries (TensorFlow, PyTorch, scikit-learn) to understand model packaging requirements.
Required Skills
Complex Problem Solving: Agile diagnostic skills when dealing with critical production incidents (AWS service outages, Git pipeline failures, or model performance degradation). Critical Thinking and Optimization: Creativity in designing cloud architectures that balance processing speed, resilience, and computational cost efficiency. Multidisciplinary collaboration: Ability to communicate effectively and act as a technical facilitator between Data Scientists (who build the model), Data Engineers (who provide the data), and traditional software development teams. Continuous learning: Curiosity and proactivity in adopting new technologies from the cloud and MLOps ecosystems that accelerate the company’s delivery cycles.
Role Mission
Design, build, and maintain the automated cloud architecture and infrastructure (AWS) for the efficient, scalable, and secure deployment of machine learning models and associated services. Serve as the technical bridge that automates the software and AI lifecycle, ensuring consistency through robust data contracts and advanced Git-based CI/CD workflows.
Role Responsibilities
Design and optimize the AI/ML infrastructure: Manage AWS environments to ensure high availability and scalability of models in production, optimizing the use of computational resources and costs.
Deployment Automation (CI/CD): Implement and maintain Git processes (GitOps/Webhooks) that automatically trigger testing, artifact building, and the deployment of the various requested services on AWS.
Data Governance and Contracts: Define, validate, and implement clear data contracts between source (Data Engineering) and destination (ML Models) teams to ensure that input streams strictly comply with technical specifications before consumption.
Implementation of MLOps Practices: Automate continuous training and retraining pipelines, and integrate monitoring of models in production to detect data drift or accuracy drops.
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