Python Junior jobs in Brazil

Join a team focused on building production-grade machine learning systems that power data-driven decision-making across large, complex datasets. In this role, you will own ML models and services end-to-end—from exploration and prototyping through deployment, monitoring, and continuous improvement—while collaborating closely with data science, product, engineering, and operations teams.

• Architect, implement, and maintain machine learning models—including gradient-boosted trees, neural networks, forecasting models, and transformers.

• Use Python and the modern data science ecosystem (NumPy, pandas, polars, Scikit-learn, PyTorch, XGBoost, Jupyter, visualization tools).

• Explore and analyze large structured datasets, particularly multivariate time-series and billing/operational data.

• Engineer high-quality features, assess data assumptions, and iterate to improve model performance.

• Develop clean, scalable code and internal APIs (e.g., FastAPI) for both online and batch inference.

• Integrate ML services into existing systems and workflows.

• Apply best practices in version control, documentation, code reviews, and test-driven development.

• Ensure reliability, clarity, and long-term maintainability of ML codebases.

• Design and manage CI/CD pipelines for ML workloads (e.g., GitHub Actions).

• Build and maintain containerized deployments using Docker and Kubernetes (or similar tools).

• Implement monitoring, logging, and experiment tracking with platforms such as MLflow, TensorBoard, Datadog, Neptune, or Weights & Biases.

• Work with relational and analytical data stores (Postgres, parquet, DuckDB).

• Partner with data engineering teams on SQL/dbt-based pipelines for training, validation, and production scoring.

• Use LLM APIs and tooling (e.g., OpenAI, Cursor) to integrate large language models into products, workflows, and pipelines where they deliver measurable value.

• Own the entire ML lifecycle: problem framing, exploration, modeling, evaluation, deployment, monitoring, retraining, and decommissioning.

• Identify technical debt and drive ongoing improvements in performance and reliability.

• Communicate complex ML concepts to both technical and non-technical audiences.

• Document findings and architectural decisions.

• Mentor junior data scientists and engineers to elevate team capabilities.

• Strong analytical and problem-solving skills grounded in machine learning principles.

• Advanced Python expertise and deep knowledge of the data science ecosystem (NumPy, pandas, polars, Scikit-learn, PyTorch, XGBoost, Jupyter).

• Experience deploying tree-based models and deep learning models in production.

• Hands-on experience with structured multivariate time-series data.

• Proficiency with Linux, Git, Bash, and cloud or high-performance computing environments.

• Experience with CI/CD pipelines, Docker, and Kubernetes for ML workloads.

• Familiarity with experiment tracking, monitoring, and logging tools for ML systems.

• Comfort working with SQL, relational databases (e.g., Postgres), and analytical formats/engines (parquet, DuckDB).

• Experience integrating and prompting LLM APIs for data and workflow automation.

• Strong written and verbal communication skills and a track record of effective cross-functional collaboration.

• Interest in mentoring others and improving engineering/ML practices across the team.

• Bachelor’s, Master’s, or Ph.D. in a quantitative discipline—or equivalent experience demonstrating senior-level ML engineering capability.

• 5+ years of professional Python development focused on data science and ML.

• 3+ years building and deploying end-to-end ML solutions in production.

• 3+ years working with deep learning or decision-tree-based methods.

• 2+ years working with structured multivariate time-series datasets.

• Demonstrated experience with:

  • CI/CD for ML workloads

  • Docker and Kubernetes (or similar orchestration)

  • Linux-based cloud or high-performance training environments

• Ph.D. or equivalent research experience in advanced ML.

• Experience with logistics, supply chain, or operational datasets.

• Deep expertise in transformers, advanced forecasting methods, or unsupervised learning for structured data.

• Publications, conference talks, or notable open-source contributions demonstrating ML innovation.

• Experience building LLM-powered tools or applications using APIs and modern LLM frameworks.