Senior AI Platform Engineer
BrazilFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- AWSPythonKubeflowKubernetesSparkCI/CDTerraformMLOps
Requirements
- Strong experience in AI/ML platform engineering, DevOps, or infrastructure engineering roles
- Hands-on expertise with Kubeflow
- Hands-on expertise with Python
- Solid experience with Kubernetes
- Solid experience with Spark
- Solid experience with AWS
- Solid experience with cloud-native architectures
- Strong knowledge of CI/CD pipelines
- Strong knowledge of Infrastructure as Code (Terraform/Crossplane)
- Strong knowledge of 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
Responsibilities
- 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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