MLOps Engineer

Collaboration

• Work closely with Machine Learning Engineers to industrialize research workflows.

• Collaborate with Software Engineers to integrate AI capabilities into operational products.

• Help define engineering standards, tooling and best practices across the Data & AI team.

• Contribute to the evolution of Alta Ares' AI platform roadmap.

Product Ownership

• Build products, not just infrastructure.

• Focus on developer experience and operational excellence.

• Deliver solutions that scale across teams and customers.

Collaboration

• Work effectively across AI, Software and Infrastructure teams.

• Communicate clearly and document engineering decisions.

• Contribute to a strong engineering culture built around quality and continuous improvement.

Deployment & Edge AI

• Build CI/CD pipelines for machine learning models.

• Deploy AI services across cloud, on-premise and edge environments.

• Optimize inference pipelines for Nvidia Jetson and embedded hardware.

• Manage model packaging, versioning and rollout strategies.

• Improve deployment reliability across operational environments.

Candidate Profile

• 3–5+ years of experience as an MLOps Engineer, ML Platform Engineer, Software Engineer or Data Engineer working on production machine learning systems.

• Strong Python software engineering skills.

• Experience designing and operating production-grade ML infrastructure.

• Solid understanding of Docker, Linux and containerized environments.

• Experience with ML experiment tracking, model registries and reproducible workflows.

• Experience designing APIs and backend services supporting AI applications.

• Familiarity with CI/CD, infrastructure automation and cloud-native development.

• Strong ownership mindset with the ability to drive projects from design to production.

• Comfortable collaborating across Machine Learning, Software and Infrastructure teams.