Machine Learning Engineer profile with solid experience deploying Data Science
models into production. The candidate will be responsible for building, validating,
deploying, and monitoring the infrastructure that supports the model lifecycle,
working in collaboration with the Data Scientists team.
This role requires a profile with a software engineering mindset applied to ML: it is
not enough to train models — real experience operating automated pipelines,
managing reproducible environments, and ensuring that production systems are
monitored and reliable is required
Responsabilidades e atribuições
Technical Fundamentals
Advanced mastery of Python as the primary and sole development language.
Object-oriented programming: design and implementation of well-structured
classes; not just scripting.
Functional knowledge of ML libraries ( Scikit-Learn , XGBoost , LightGBM ,
TensorFlow , PyTorch ) oriented toward packaging and serving models.
Advanced handling of tabular data with Polars ; distributed processing with
PySpark.
Advanced SQL and data handling in Hadoop/Hive ecosystems.
Data connections from Python: SQLAlchemy , ODBC / JDBC .
MLOps and Infrastructure
Experience building and operating automated ML pipelines: training,
validation, and deployment.
Handling of MLflow or equivalent tool for experiment tracking and model
registry.
Containerization with Docker: building images for training and serving.
Data and model versioning with DVC or equivalent.
Cloud: GCP (Vertex AI, GCS, Artifact Registry) and Azure (Azure ML, Blob
Storage, ACR).
Experience implementing CI/CD for ML projects.
Experience with production model monitoring: degradation detection, alerts,
and response.
Orchestration with Apache Airflow: DAG design, operators, dependency
management, and alerts. (Desirable)
Development Discipline
Professional use of Git: the candidate must demonstrate a disciplined work
history, not just knowledge of commands.
Experience writing automated tests ( Pytest ) for data pipelines and
transformations.
Secure handling of credentials and secrets in ML projects.
Judgment to define, communicate, and drive the adoption of standards and
best practices among other team profiles, ensuring quality delivery.
Judgment on AI
Responsible use of generative AI tools as development assistants, with the
ability to audit the code they produce.
Requisitos e qualificações
Recommended Experience
2 or more years of experience in ML Engineering, Data Engineering, or Software
Engineering roles with a focus on Machine Learning systems, or in Data Science
roles with a strong orientation toward engineering and the complete model
lifecycle (including experimentation, versioning, monitoring, and pipeline
automation)
Models taken to production with documented automated pipelines (not just
prototypes or notebooks).
Experience collaborating with Data Scientists to receive experiments and
convert them into production systems.
Work history in real code repositories (a shareable portfolio will be valued).
Experience in agile environments (Scrum, Kanban). (Desirable)
Academic Background
Bachelor’s or Master’s degree in: Computer Science, Software Engineering,
Mathematics, Applied Mathematics, Mechanical Engineering, or related fields.
Informações adicionais
Notes for the Search
The selection process includes a practical technical evaluation and review
of the candidate’s previous work.
The ability to identify problems in existing code and propose solutions will be
valued, not just producing new code.
We are not looking for profiles who know tools superficially: we are looking for
candidates who can operate and make justified design decisions in a real
production environment.
The role involves defining and enforcing technical standards for the team —
technical leadership in that dimension is expected.
Etapas do processo
- Etapa 1: Cadastro
- Etapa 2: Presentación de CV
- Etapa 3: Entrevistas
- Etapa 4: Confirmación de candidato
- Etapa 5: Onboarding
- Etapa 6: Contratação
Creer para co-crear
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