Senior MLOps Engineer

New

Fully remote position within BrazilFull-TimeSenior

Salary not disclosed

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Job Details

Strong experience in DevOps and MLOps, including CI/CD pipelines, infrastructure as code, and observability systems.
Hands-on expertise with Kubernetes, Kubeflow, Spark, and AWS cloud environments.
Strong Python development skills, with experience building reusable libraries, APIs, and platform tooling.
Proven ability to design and operate large-scale ML or data platforms in production environments.
Experience working closely with Data Science teams to enable scalable ML workflows and deployment pipelines.
Solid understanding of distributed systems and cloud-native architecture principles.
Strong systems thinking mindset with the ability to balance platform standards and team autonomy.
Excellent collaboration and communication skills in cross-functional engineering environments.

Design, evolve, and maintain scalable ML infrastructure, including Kubeflow, Feast, and Spark-on-Kubernetes environments.
Build internal platforms, tools, and APIs that enable self-service ML development and deployment across distributed teams.
Define and implement MLOps and LLMOps best practices, supporting the full lifecycle from experimentation to production.
Collaborate with Data Science and engineering teams to standardize CI/CD, versioning, testing, and observability for ML workflows.
Develop and optimize cloud-native solutions using AWS, Kubernetes, and infrastructure-as-code tools such as Terraform or Crossplane.
Ensure seamless integration of ML artifacts into enterprise data catalog, privacy, and governance systems.
Improve developer experience by creating reusable libraries, abstractions, and scalable orchestration frameworks.
Partner cross-functionally to translate complex ML use cases into robust, production-ready platform solutions.

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