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.

This is a high-impact platform engineering role focused on building and scaling the infrastructure that powers large-scale machine learning and AI systems across a global organization. You will work at the intersection of distributed systems, MLOps, and cloud-native engineering, enabling data scientists and engineers to build, deploy, and operate AI solutions efficiently. The role involves evolving core ML platforms, improving developer experience, and designing self-service capabilities that reduce friction across the AI lifecycle. You will contribute to the design and optimization of production-grade ML infrastructure supporting thousands of users and complex workloads. The environment is highly collaborative and innovation-driven, with strong emphasis on scalability, reliability, and engineering excellence. This position is ideal for a software-minded platform engineer passionate about AI systems, automation, and modern cloud technologies. Your work will directly shape how AI products are built and delivered at scale.

In this role, you will design, build, and evolve the core AI and ML platform infrastructure, ensuring scalability, reliability, and a seamless developer experience for engineering and data science teams. You will contribute to MLOps and LLMOps lifecycle orchestration while enabling self-service capabilities across distributed teams.

• Evolve and maintain ML platform infrastructure including Kubeflow, Feast, Spark-on-Kubernetes, and AWS-based systems
• Design and implement internal tools, APIs, and abstractions to enable self-service ML workflows
• Build scalable CI/CD, versioning, and deployment frameworks tailored to ML and AI use cases
• Drive MLOps and LLMOps best practices across the organization, from experimentation to production
• Collaborate with data science and engineering teams to standardize production-grade AI workflows
• Improve developer experience by treating the platform as a product with continuous feedback loops
• Partner with infrastructure and data teams to integrate ML systems into governance, catalog, and privacy frameworks
• Ensure observability, reliability, and performance of large-scale AI workloads in production environments

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Requirements:

This role requires strong experience in platform engineering, distributed systems, and ML infrastructure, combined with a product mindset and deep technical expertise in cloud-native and DevOps practices. The ideal candidate is a systems thinker who thrives in complex, high-scale environments.

• Strong experience in software or platform engineering with focus on ML/AI systems
• Hands-on expertise with Kubernetes, Kubeflow, Spark, and AWS ecosystems
• Strong proficiency in Python for building scalable libraries, APIs, and tooling
• Experience with CI/CD, Infrastructure as Code (Terraform or Crossplane), and observability tools
• Solid understanding of MLOps practices and modern AI/ML lifecycle management
• Ability to design scalable, reusable platform solutions and developer-facing tools
• Experience collaborating with data science or analytics teams in production environments
• Strong systems thinking and ability to connect infrastructure, data, and governance layers
• Product mindset focused on developer experience and platform usability
• Strong communication and collaboration skills in distributed engineering environments

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Benefits:

• Flexible remote-first work model within Brazil
• Health, dental, and life insurance coverage
• Free premium wellness membership with access to gyms, fitness programs, and mental health resources
• Comprehensive emotional wellbeing program including therapy sessions and support tools
• Flexible working schedule adapted to personal and team needs
• Paid parental leave and extended family support policies
• Generous paid time off including vacation, additional days off, and birthday leave
• Home office setup support and reimbursement
• Career development platforms, learning resources, and internal mobility opportunities
• Inclusive, diverse, and collaborative global engineering culture