Cadastra

Machine Learning Engineer, Mid-level

Cadastra

full-time

Posted on:

Location Type: Remote

Location: Brazil

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Tech Stack

AirflowAWSAzureCloudDockerFlaskGoogle Cloud PlatformKubernetesPySparkPythonSparkSQL

About the role

  • Model Operationalization (MLOps): Work on deploying Machine Learning models, turning prototypes (notebooks) into scalable, production-ready products.
  • Pipeline Construction: Develop and maintain automated training, validation and inference pipelines (batch and real-time).
  • Code Quality: Refactor modeling code with a focus on performance, readability and software engineering best practices (unit testing, modularization).
  • Monitoring: Implement model monitoring strategies in production (data drift, concept drift and API performance).
  • Infrastructure: Manage and optimize compute resources in a cloud environment (GCP) for running ML jobs.
  • Model Governance: Keep experiment tracking and model versioning (Model Registry) organized.
  • Collaboration: Act as a technical bridge between Data Science and Data Engineering teams.

Requirements

  • Strong Programming: Proficiency in Python with a focus on Software Engineering (Object-Oriented Programming, Design Patterns) and SQL.
  • Cloud Computing: Experience with cloud environments (AWS, Azure or GCP).
  • ML Lifecycle: Clear understanding of the ML model lifecycle (from training to serving).
  • MLOps Tools: Hands-on experience with experiment tracking and registry tools (e.g., MLflow, Weights & Biases).
  • Containerization: Knowledge of Docker for creating images and reproducible environments.
  • APIs: Experience developing APIs to serve models (FastAPI or Flask).
  • Versioning: Fluent use of Git and collaborative workflows.
  • Desirable: Knowledge of workflow orchestrators (Airflow or similar).
  • Experience with CI/CD applied to Machine Learning (CML, GitHub Actions).
  • Familiarity with Feature Stores.
  • Basic knowledge of Kubernetes.
  • Experience with distributed processing libraries (Spark/PySpark).
  • Previous experience deploying NLP or Computer Vision models in production.
  • Behavioral: Hands-on profile with a focus on automation.
  • Ability to translate data scientists' requirements into robust technical solutions.
  • Technical curiosity to test new tools in the MLOps ecosystem.
  • Strong communication skills to align expectations across technical teams.
Benefits
  • Meal and food allowance on FLASH card 🥗
  • Home office allowance on FLASH card 💳
  • Health insurance 🩺
  • Dental plan 🦷
  • Birthday day off + credit deposited to FLASH card 🎉
  • Extended maternity and paternity leave 🍼
  • Profit sharing (PLR) 💰
  • Life insurance 🧡
  • Childcare assistance 👶
  • Referral bonus 💰
  • Transportation voucher 🚍
  • Clude | Health Platform 🩺
  • Total Pass 🏋🏽‍♀️
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
PythonSQLMLOpsMachine LearningDockerAPIsGitCI/CDSparkKubernetes
Soft Skills
collaborationcommunicationtechnical curiosityautomationproblem-solving