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Mutt Data

Delivers ML solutions for business outcomes

Machine Learning Engineer Databricks

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeRemote in Argentina

About the Job

Requirements

  • Proven experience orchestrating data and machine-learning pipelines on Databricks, including Jobs, Workflows, and cluster policies.
  • Solid continuous integration and continuous delivery experience with Azure DevOps, gitflow, and pull requests; infrastructure as code with Databricks Asset Bundles; and code-quality standards using black, isort, and mypy.
  • Advanced Python, SQL, Delta, and Unity Catalog skills.

Responsibilities

  • Orchestrate production pipelines on Databricks Jobs, chaining tasks via depends_on and managing failure modes including ALL_SUCCESS and ALL_DONE.
  • Configure and maintain deployments via Databricks Asset Bundles, promoting across dev, qa, and prd targets, with resource and deployment files per country.
  • Manage the lifecycle of models and artifacts in MLflow and Unity Catalog, including registration, versioning, Champion/Challenger aliases, and rollback.
  • Ensure operational continuity by managing compute across workspaces, standardizing cost-monitoring tags for FinOps, and integrating with AIOps and observability.
  • Master the product end-to-end to diagnose runtime incidents and coordinate with development squads.

About the Company

Mutt Data

Delivers ML solutions for business outcomes

Mutt Data helps startups and large enterprises build and implement machine learning solutions that deliver real business results. They work across finance, insurance, advertising, telecoms, on-demand services, and e-commerce to help clients get ahead using the latest methods and best practices. How it works: They provide end-to-end ML services, starting with understanding business goals, data assessment, data engineering, model development, deployment, and ongoing monitoring and optimization. They tailor tools and techniques to each industry to put models into production and track their impact. Differences: They serve both startups and big companies and offer a complete workflow from idea to production, across many industries, with a focus on practical business outcomes rather than research-only efforts. Goal: Help customers gain a competitive edge by turning data into deployable ML solutions that improve efficiency, revenue, and user experience.

Company size
51-200
Company stage
Not listed
Total funding
Not listed
Headquarters
Capital, Argentina
Founded
2017

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