Engenheiro de Dados Sênior MLOps

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Engenheiro de Dados Sênior MLOps based in Brazil.

We are looking for a senior data professional to design, implement, and evolve machine learning operations in production environments. This role focuses on building scalable, secure, and governed solutions that enable reliable AI model deployment and lifecycle management. The position requires strong expertise in modern data platforms, automation, and MLOps best practices. You will collaborate closely with Data Scientists, Data Engineers, Analytics teams, and business stakeholders to deliver impactful solutions. The ideal candidate will help transform data-driven initiatives into efficient and sustainable production capabilities. This is an opportunity to work in a highly collaborative environment where innovation, automation, and technical excellence are key drivers.

Accountabilities:: The role is responsible for ensuring the successful deployment, operation, and continuous improvement of machine learning solutions, supporting scalable data architectures and efficient AI delivery processes. Key responsibilities include:

Develop and maintain MLOps pipelines for model deployment, management, and operationalization.

Build data preparation, transformation, and validation pipelines using PySpark and Databricks.

Deploy, version, and manage the lifecycle of machine learning models in production environments.

Automate model training, validation, deployment, and monitoring processes.

Implement CI/CD practices for machine learning projects using GitHub and related tools.

Monitor model performance, availability, data quality, and system stability.

Ensure model traceability, governance, and reproducibility throughout the entire lifecycle.

Integrate Azure, Databricks, and other components within modern data architectures.

Collaborate with multidisciplinary teams to deliver scalable data and AI solutions.

Identify opportunities to improve performance, optimize costs, and increase operational efficiency.

Requirements:

The ideal candidate should have strong technical expertise in data engineering, machine learning operations, and cloud-based platforms, combined with the ability to collaborate across technical and business teams. Required qualifications include:

Solid experience deploying and operationalizing machine learning models in production environments.

Advanced experience with Databricks.

Strong knowledge of Azure cloud services.

Advanced proficiency in PySpark for distributed data processing.

Experience with GitHub and software version control practices.

Knowledge of MLOps concepts, including automation, monitoring, governance, and model lifecycle management.

Experience building and maintaining data and machine learning pipelines.

Experience working with production environments and scalable architectures.

Knowledge of CI/CD practices applied to machine learning projects.

Experience with model monitoring, data quality management, and observability practices.

Strong analytical thinking, problem-solving skills, and ability to work collaboratively with different teams.

Preferred qualifications include:

Experience with MLflow, Azure Machine Learning, or similar tools.

Knowledge of Lakehouse architecture concepts.

Experience working in agile and technology-driven environments.

Benefits:

Health and dental insurance.

Meal and food allowance.

Childcare assistance.

Extended parental leave.

Access to gyms and wellness professionals through Wellhub and TotalPass partnerships.

Profit Sharing and Results Participation (PLR).

Life insurance.

Continuous learning platform with professional development resources.

Discount club partnerships.

Free online platform focused on physical, mental, and overall well-being.

Pregnancy and responsible parenting courses.

Partnerships with online learning platforms.

Language learning opportunities.

Additional benefits and employee support programs.

How 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.     #LI-CL1