Senior Machine Learning Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Machine Learning Engineer based in Brazil.

This role offers the opportunity to lead the development, industrialization, and evolution of Machine Learning solutions at enterprise scale. You will work at the intersection of Data Science, Data Engineering, and MLOps, taking ownership of ML solutions throughout their lifecycle. The position combines hands-on engineering with architectural leadership, covering model deployment, feature management, monitoring, governance, and continuous improvement. You will help transform analytical models into scalable, production-ready solutions that are reliable, observable, and sustainable. The role also involves establishing engineering standards, supporting Data Scientists, and advancing the organization’s data and MLOps capabilities. You will collaborate with global teams in a technically sophisticated environment where innovation and operational excellence are central to delivering business impact.

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Accountabilities:

• MLOps Leadership: Lead MLOps initiatives covering model training, deployment, serving, monitoring, lifecycle management, and governance for production Machine Learning solutions.

• Data & ML Pipelines: Develop and maintain ETL/ELT pipelines, DAGs, data workflows, and Machine Learning workflows using PySpark and distributed processing technologies.

• Feature Management: Design and manage enterprise Feature Stores, ensuring feature versioning, lineage, consistency between training and inference, and reliable point-in-time lookups.

• Model Industrialization: Develop, validate, deploy, and operationalize Machine Learning models across different analytical use cases, supporting their transition from experimentation to production.

• Model Lifecycle: Implement model versioning, Champion/Challenger strategies, rollouts, model promotion processes, and Model Registry management.

• Observability & Governance: Ensure quality, traceability, reproducibility, auditability, and governance across data, features, pipelines, and models, including monitoring for data, concept, and performance drift.

• CI/CD & Infrastructure: Design and implement CI/CD processes and Infrastructure as Code for Machine Learning platforms and manage DEV, QA, and PROD environments.

• Engineering Standards: Define architectural standards, MLOps guidelines, engineering best practices, automated testing approaches, and sustainable development patterns.

• Technical Leadership: Conduct code reviews, support Data Scientists in industrializing ML solutions, contribute to architectural decisions, and help drive the continuous evolution of the ML platform.

• Documentation & Knowledge Sharing: Maintain clear technical, architectural, and operational documentation while promoting knowledge sharing across teams.

Requirements:

• Databricks: Advanced experience with Databricks, including MLflow, Unity Catalog, Delta Lake, Databricks Workflows, Model Registry, Model Serving, and Databricks Asset Bundles (DABs).

• Production ML: Strong experience developing, operationalizing, monitoring, and supporting Machine Learning models in production environments.

• Machine Learning: Solid knowledge of Feature Engineering, hyperparameter optimization, model evaluation, and supervised and unsupervised learning algorithms.

• Feature Stores: Practical experience with enterprise Feature Stores, including feature versioning, lineage, consistency, and point-in-time lookups.

• ML Observability: Knowledge of Data Drift, Concept Drift, Performance Drift, and observability practices for data and Machine Learning pipelines.

• Programming & Data: Strong proficiency in Python, PySpark, SQL, MLflow, Spark MLlib, and relevant Machine Learning ecosystem libraries.

• Data Engineering: Experience with distributed processing and Spark workload optimization, including the ability to design efficient and scalable data workflows.

• DevOps & CI/CD: Experience building CI/CD pipelines, managing multiple deployment environments, and implementing Infrastructure as Code.

• Testing: Experience implementing automated testing for data pipelines and Machine Learning workflows.

• Security: Knowledge of secure credential and secrets management using Service Principals, Key Vault, or equivalent solutions.

• Development Tools: Experience with Azure DevOps or equivalent development and delivery platforms.

• English: Intermediate English proficiency, with the ability to interact with global teams and produce technical documentation in English.

• Technical Mindset: Strong hands-on engineering skills combined with an architectural mindset, ownership mentality, problem-solving ability, and focus on building governed, observable, reproducible, and sustainable ML solutions.

• Certifications & Advanced AI: Databricks Certified Machine Learning Professional certification is highly desirable. Databricks Data Engineer Professional certification and experience with GenAI, LLMOps, or RAG architectures are additional advantages.

Benefits:

• Healthcare: Health and dental insurance.

• Food Allowances: Meal and food allowances.

• Family Support: Childcare assistance and extended paternity leave.

• Wellness: Access to gyms and health and wellness professionals through Wellhub and TotalPass.

• Profit Sharing: Profit Sharing and Results Participation (PLR).

• Insurance: Life insurance coverage.

• Continuous Learning: Access to a continuous learning platform and partnerships with online learning providers.

• Language Development: Language learning platform.

• Discounts: Access to a discount club.

• Well-Being: Free online resources dedicated to physical, mental, and overall well-being.

• Parenting Support: Pregnancy and responsible parenting course.

• Inclusive Environment: Dedicated health and well-being resources, inclusion specialists, and affinity groups supporting employees throughout their journey.

• Accessibility Support: Support and accommodations are available for professionals with disabilities throughout the selection process.

\nHow Jobgether works:

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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