Company
Technology
SeniorMLOpsEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior MLOps Engineer. Skills: MLOps, Kubernetes, Cloud-native, Platform engineering. Design scalable ML infrastructure. Evolve ML infrastructure”
Industry & Context.
Systems thinking; Problem solving
What They're Looking For.
Must Have
Experience in DevOps and MLOps, CI/CD pipelines, Infrastructure as code, Observability systems, Hands-on expertise with Kubernetes, Kubeflow, Spark, AWS cloud environments, Python development skills, Building reusable libraries, APIs, Platform tooling, Design and operate large-scale ML or data platforms, Production environments, Working closely with Data Science teams, Scalable ML workflows, Deployment pipelines, Solid understanding of distributed systems, Cloud-native architecture principles, Systems thinking mindset, Balance platform standards and team autonomy, Excellent collaboration and communication skills
Nice to Have
Experience with LLMOps, Advanced ML orchestration, Data governance frameworks
What You'll Do.
Design scalable ML infrastructure
Evolve ML infrastructure
Maintain ML infrastructure
Build internal platforms
Enable self-service ML development
Enable self-service ML deployment
Define MLOps best practices
Define LLMOps best practices
Implement MLOps best practices
Implement LLMOps best practices
Support full lifecycle
Standardize versioning
Standardize observability
Develop cloud-native solutions
Optimize cloud-native solutions
Ensure seamless integration
Improve developer experience
Create reusable libraries
Create scalable orchestration frameworks
Translate ML use cases
Create robust platform solutions
Create production-ready platform solutions
How You'll Work.
Team & Collaboration
Distributed teams; Data Science teams; Engineering teams; Cross-functional teams
Communication Scope
Collaboration skills
Process & Methodology
Platform standards, Team autonomy
Full Job Description
## Accountabilities 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. Requirements: 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. Nice to have: experience with LLMOps, advanced ML orchestration, or data governance frameworks. Benefits: Fully remote position within Brazil with flexible work arrangements. Comprehensive health, dental, and life i
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