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Tech Stack
Tools & technologiesPythonPyTorchScikit-LearnSparkSQLTensorflow
About the role
Key responsibilities & impact- Develop and maintain model training, validation and inference pipelines.
- Design feature and data pipelines that support machine learning applications in production.
- Convert models developed in exploratory environments into reproducible, scalable pipelines.
- Integrate machine learning solutions into the organization’s data ecosystem.
- Collaborate with engineering, DevOps and DataOps teams to evolve the platform and the organization’s machine learning practices.
- Work closely with data scientists to operationalize analytical models.
- Support the transition of models from exploratory environments to production pipelines.
- Build pipelines that enable recurring training, updating and model evolution.
- Ensure models can run at scale and meet performance requirements for operational use.
- Prepare, structure and transform the data required for model training and execution.
- Develop data pipelines and feature engineering focused on machine learning applications.
- Work with data engineering to ensure the availability and quality of the data used by models.
- Implement monitoring mechanisms for model performance, stability and behavior in production.
- Support the identification of model degradation and the need for retraining.
- Contribute to the continuous improvement of machine learning solutions in production.
- Ensure software development best practices are applied to machine learning solutions.
- Use code versioning, code review and engineering practices for developing ML pipelines.
- Contribute to the standardization and evolution of the area’s machine learning engineering practices.
Requirements
What you’ll need- Experience developing in Python for data and machine learning applications.
- Experience designing data pipelines and machine learning pipelines.
- Experience with data manipulation and processing using SQL and distributed environments (e.g., Spark).
- Experience with code versioning and software engineering best practices (Git).
- Experience operationalizing machine learning models in production environments.
- Experience with machine learning libraries and frameworks (e.g., scikit-learn, TensorFlow, PyTorch or similar).
- Experience integrating analytical solutions with data pipelines or applications.
Benefits
Comp & perks- Food allowance
- Meal voucher
- Transportation voucher
- Profit-sharing program (PPR)
- Railway safety bonus
- Private pension plan
- Credit union
- Payroll-deductible loan
- Wellhub
- Health insurance
- Dental plan
- Surgical instrumentation
- Pharmacy assistance
- Life insurance
- Funeral assistance
- Support program from pregnancy through postpartum
- Extended parental leave
- School supplies
- Childcare assistance
- Christmas toy
- Christmas gift kit
- Psychological, legal and financial assistance program
- Benefits and discounts club
- Corporate university
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
PythonSQLSparkmachine learningdata pipelinesfeature engineeringmodel trainingmodel validationmodel inferencecode versioning
Soft Skills
collaborationcommunicationproblem-solvingcontinuous improvementorganizational skills