This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Data Science Enablement Specialist | ML Platform & MLOps | Especialista in Brazil.

This role sits at the intersection of machine learning engineering, platform enablement, and developer advocacy, supporting the adoption of a large-scale ML platform across multiple data science and engineering teams. You will act as a key technical enabler, ensuring that ML practitioners can effectively build, deploy, and monitor models in production environments with consistency and best practices. The position combines deep MLOps expertise with strong communication and enablement skills, making it essential for scaling machine learning maturity across the organization. You will work closely with product, engineering, and data science teams to remove friction in adoption, improve platform usability, and define reference architectures. The environment is highly technical, collaborative, and fast-paced, with strong emphasis on production-grade ML systems. This is a high-impact role where your work directly influences how ML is built, deployed, and operated at scale.

You will be responsible for enabling machine learning teams to effectively adopt and maximize a large-scale ML platform, while ensuring technical excellence, scalability, and production readiness across all use cases.

• Lead technical enablement for ML teams using the enterprise ML platform, ensuring best practices across the full model lifecycle (training, deployment, serving, monitoring).
• Coordinate and support the migration of legacy ML models to the new platform, ensuring stability, performance, and no regression in production.
• Identify technical gaps, bugs, documentation issues, and adoption barriers, working closely with product and engineering teams to drive resolutions.
• Create high-quality technical documentation, including architecture blueprints, runbooks, code examples, and implementation guides.
• Translate community and user feedback into actionable insights to influence platform roadmap and prioritization.
• Conduct technical workshops, enablement sessions, and architecture reviews with data science and engineering teams.
• Act as a key technical reference point between the platform team and its internal user community.

Requirements:

The ideal candidate combines strong MLOps expertise with hands-on ML engineering experience and a passion for enabling other teams through clear documentation, architecture design, and technical leadership.

• Strong experience with ML platforms and end-to-end machine learning lifecycle (training, validation, deployment, serving, monitoring).
• Deep knowledge of MLOps practices in large-scale production environments.
• Proven experience in technical enablement, ML architecture, developer advocacy, or solution architecture roles.
• Ability to produce clear technical documentation and reference architectures for engineering and data science teams.
• Strong ownership mindset with the ability to identify critical issues and drive solutions independently.
• Advanced Python skills for ML workflows, including reviewing and guiding production-level code.
• Experience with tools such as MLflow, Kubeflow, or equivalent MLOps platforms.
• Experience with cloud ML platforms such as AWS SageMaker, Vertex AI, or Azure ML (preferred).
• Background in data-mature environments such as fintechs, banks, or large-scale tech organizations (preferred).

Benefits:

• Competitive compensation aligned with market standards
• Remote-friendly work model
• Career growth opportunities in AI and machine learning domains
• Access to advanced AI and ML tools and platforms
• Learning and development programs focused on emerging technologies
• Collaborative and highly technical work environment
• Opportunity to influence large-scale ML platform adoption and strategy

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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