Senior AI Platform Engineer

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Senior AI Platform Engineer in Brazil.

You will join a global data and product engineering organization building the foundations that power AI at scale across hundreds of engineers and data scientists. In this role, you will design and evolve a cloud-native AI platform that supports both traditional machine learning and emerging LLM-driven applications. You will work at the intersection of infrastructure, DevOps, and machine learning engineering, enabling seamless model development, deployment, and monitoring. Your work will directly impact how AI products are built, shipped, and scaled across global teams. You will help transform fragmented workflows into a unified self-service platform focused on developer experience and reliability. Operating in a remote-first, collaborative environment, you will partner with infrastructure, data, and ML teams worldwide. This is a high-impact role where platform engineering decisions directly shape the speed and quality of AI innovation.

You will be responsible for evolving and scaling a modern AI platform that enables ML and data science teams to build and deploy at scale, while ensuring reliability, automation, and best-in-class developer experience:

• Design, build, and maintain cloud-native AI/ML infrastructure, including Kubeflow, Spark-on-Kubernetes, and related orchestration systems
• Develop internal tools, APIs, and abstractions that enable self-service ML lifecycle management across distributed teams
• Implement and improve MLOps and LLMOps practices, ensuring smooth transitions from experimentation to production-grade deployment
• Standardize engineering practices across ML workflows, including CI/CD, testing, versioning, observability, and release automation
• Collaborate with Data Science, Data Engineering, and Infrastructure teams to integrate ML systems into broader data governance and catalog ecosystems
• Drive platform reliability, scalability, and performance across AWS-based Kubernetes environments
• Act as a technical bridge between infrastructure and ML teams, ensuring alignment on architecture and delivery needs
• Promote a platform-as-a-product mindset focused on usability, automation, and continuous improvement

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Requirements

You are a strong software and platform engineer with deep experience building scalable ML infrastructure and enabling data science teams in production environments:

• Strong experience in AI/ML platform engineering, DevOps, or infrastructure engineering roles
• Hands-on expertise with Kubeflow and Python (mandatory)
• Solid experience with Kubernetes, Spark, AWS, and cloud-native architectures
• Strong knowledge of CI/CD pipelines, Infrastructure as Code (Terraform/Crossplane), and observability practices
• Experience building MLOps workflows and supporting production machine learning systems at scale
• Familiarity with LLMOps concepts and modern AI lifecycle management approaches
• Strong software engineering skills with a focus on reusable libraries, APIs, and tooling
• Ability to collaborate with data science teams and translate ML needs into scalable platform solutions
• Strong systems thinking, with the ability to design end-to-end architecture across multiple teams
• Excellent communication skills and a collaborative, product-oriented mindset

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Benefits

• Flexible-first remote work model within Brazil
• Comprehensive health, dental, and life insurance coverage
• Free wellness platform membership with fitness, mindfulness, and mental health support
• Emotional wellbeing program with therapy sessions and on-demand resources
• Home office support and setup reimbursement
• Flexible working hours aligned with team and personal needs
• Generous paid time off, including vacation, additional days off, and birthday leave
• Paid parental leave with extended support for new parents
• Strong career development programs, internal mobility, and continuous learning opportunities
• Inclusive, collaborative culture focused on innovation and work-life balance