Machine Learning Platform

This is a fully remote position, open to applicants in Argentina.

📋 Description

• Develop infrastructure for Machine Learning and MLOps to create, deploy, and maintain models in production.

• Create tools, SDKs, and automated pipelines ensuring reproducibility, observability, and governance.

• Build CLI tools for automated deployment (ml-deploy, ml-rollback).

• Implement automated workflows using MLflow for model promotion from development to staging and production.

• Create template repositories with best practices for models and APIs.

• Establish best practices for the productization of Bayesian models.

• Document APIs and SDKs using Sphinx/MkDocs, tutorials, and examples.

• Conduct training sessions for Data Scientists.

• Develop self-service tools to achieve over 80% autonomy for Data Scientists.

• Implement automated testing suites for model validation and performance.

• Optimize the performance of Bayesian models and MLflow in production.

• Build monitoring dashboards to track model health.

• Automate governance, approval gates, and compliance checks.

• Provide incident response support and troubleshooting assistance.

• Maintain and evolve SDKs and tooling based on feedback from Data Scientists.

• Enhance the serving architecture for complex models.

• Keep documentation, runbooks, and troubleshooting guides up to date.

⛳️ Requirements

• Over 5 years of experience in Data Science, Machine Learning, or MLOps.

• At least 2 years working with MLOps tools in production (registry, tracking, model serving, monitoring, versioning).

• Experience in building Python SDKs/libraries for internal use.

• Familiarity with CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins).

• Ability to understand Bayesian models, deploy them, and enhance their performance.

• Advanced Python skills (packaging, testing, documentation).

• Knowledge of MLflow (registry, tracking server, model serving, artifacts).

• Experience with Bayesian models (PyMC, Stan, or similar), productization, and optimization.

• Proficient in Docker and Kubernetes.

• Familiarity with cloud platforms (preferably GCP; AWS/Azure acceptable).

• Internal tooling mindset to design for fellow developers.

• Clear and comprehensive technical documentation skills.

• Effective communication with Data Scientists.

• Ownership and autonomy in work.

• Experience with Feast, Airflow/Composer, credit risk models, growth and fraud, fintech or regulated environments, open source contributions, Evidently AI or similar, Model Governance, leading developer experience initiatives, and agile methodologies is a plus.

🏝️ Benefits

• Work culture based on trust and purpose.

• Fully remote working environment.

• Space for creativity, curiosity, ideas, and feedback.

• Culture of transparency with a focus on real impact.