Senior Machine Learning Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Machine Learning Engineer based in Brazil.

We are looking for an experienced Machine Learning Engineer to help evolve and operate a globally deployed recommender <system.You> will play a key role in strengthening the architecture, deployment processes, and operational foundations that support production machine learning.The position has a strong focus on MLOps, AWS, automation, observability, and building reliable ML systems at <scale.You> will work closely with Data Scientists, Data Engineers, Software Engineers, Product Managers, and business stakeholders to turn models into dependable production capabilities.Beyond hands-on engineering, you will provide technical direction and mentorship while promoting strong software engineering and system design <practices.You> will also have the opportunity to evaluate new technologies and introduce improvements across the machine learning lifecycle.This role is well suited to a senior engineer who enjoys solving complex infrastructure challenges and shaping scalable, production-ready ML platforms.

Accountabilities

• Drive and continuously improve MLOps practices across the machine learning environment.
• Build, optimize, and maintain CI/CD pipelines using GitLab to automate reliable ML delivery.
• Implement and maintain experiment tracking and model management workflows using MLflow.
• Productionize, deploy, and maintain machine learning models using AWS, with a strong focus on SageMaker.
• Design, build, and maintain scalable machine learning and data pipelines.
• Develop and maintain robust Python-based ML and data infrastructure.
• Implement monitoring, observability, and operational practices to ensure ML systems remain reliable and performant.
• Apply software engineering best practices, including automated testing, documentation, version control, and system design.
• Provide technical guidance and mentorship to Data Scientists, Data Engineers, and MLOps Engineers.
• Collaborate closely with Product Managers, engineers, data professionals, and business stakeholders to align technical solutions with business objectives.
• Evaluate emerging technologies, tools, and methodologies that can improve machine learning capabilities and operational efficiency.
• Contribute to the continuous improvement of the ML platform and its ability to support scalable production workloads.

Requirements

• 5+ years of professional experience in Machine Learning Engineering or a closely related field.
• Strong hands-on experience deploying, operating, and maintaining production machine learning systems.
• Expert-level Python skills and strong knowledge of the broader data science and machine learning ecosystem.
• Hands-on experience with AWS cloud services, preferably including AWS SageMaker.
• Strong understanding of MLOps principles, practices, tooling, and the machine learning lifecycle.
• Practical experience with MLflow for experiment tracking and model management.
• Experience designing and maintaining GitLab CI/CD pipelines.
• Hands-on experience with at least one major deep learning framework, such as PyTorch or TensorFlow.
• Proven experience designing and building scalable ML and data pipelines.
• Experience implementing monitoring and observability for machine learning systems.
• Ability to design, document, explain, and communicate complex technical architectures to both technical and non-technical stakeholders.
• Experience mentoring engineers and data scientists and providing technical leadership.
• Strong communication, collaboration, and stakeholder management skills.
• Bachelor's degree in Computer Science, Engineering, or a related technical discipline, or equivalent practical experience.
• Master's degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field is a plus.
• Experience with Prometheus, Grafana, Evidently AI, or similar monitoring and observability technologies is preferred.
• Experience working with large-scale recommender systems is highly valued.
• Strong understanding of software engineering principles, architecture, and system design is preferred.

Benefits

• B2B contract arrangement.
• Opportunity to work on technically challenging machine learning projects with mature engineering practices.
• Exposure to modern ML technologies, AWS infrastructure, MLOps tooling, and enterprise-scale systems.
• Opportunity to contribute to a globally deployed recommender system and production ML platform.
• Collaborative and supportive environment focused on knowledge sharing and professional development.
• Opportunity to provide technical mentorship and influence engineering practices across multidisciplinary teams.
• Exposure to complex machine learning infrastructure, automation, observability, and scalable system design.
• Opportunity to evaluate and introduce new technologies that improve ML capabilities and operational efficiency.