Machine Learning Engineer

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

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.

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.

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.

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