#2 - Senior Machine Learning Engineer - Databricks

Muttdata
Mexico
Remote

Who this role is best for

A natural match if you have experience deploying ML systems on Databricks and managing multi-country data pipelines.

Best fit for

  • Candidates with experience deploying ML systems on Databricks and managing complex production pipelines across multiple regions
    — “own the deployment, job configuration, and end-to-end operation of our AI Factory products”
  • Individuals who have worked with MLflow and Unity Catalog for model lifecycle management
    — “Manage the lifecycle of models and artifacts in MLflow + Unity Catalog”
  • Professionals with a track record of CI/CD automation and IaC practices in data/ML environments
    — “Solid CI/CD experience (Azure DevOps, gitflow, PRs), IaC with DABs”

Things to consider

  • The role requires strong overlap with Mexico working hours, even though it is remote-first
    — “Currently based in Mexico or a location within a similar time zone”
  • The position involves managing production pipelines across multiple countries, which may require understanding of regional data practices
    — “making sure the full pipeline — from data generation to publishing results to operations — runs reliably, observably, and scalably across countries”

How to stand out

  • Highlight your experience with Databricks Jobs and failure mode management in your resume and interviews
    — “Orchestrate production pipelines on Databricks Jobs, chaining tasks via depends_on and managing failure modes”
  • Emphasize your ability to standardize and maintain cost-monitoring practices (FinOps) across environments
    — “manage compute across workspaces, standardize cost-monitoring tags (FinOps)”
  • Demonstrate mastery of end-to-end ML product workflows and incident diagnosis in your interview responses
    — “Master the product end-to-end to diagnose runtime incidents and coordinate with development squads”
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • Orchestrate production pipelines on Databricks
  • Manage the lifecycle of models and artifacts in MLflow + Unity Catalog
  • Ensure operational continuity: manage compute across workspaces, standardize cost-monitoring tags (FinOps)
Typical background
Proven experience orchestrating data/ML pipelines on DatabricksSolid CI/CD experience (Azure DevOps, gitflow, PRs)

Skills & requirements

Required

Databricks JobsDatabricks Asset BundlesMlflowUnity CatalogPythonSQLDeltaCI/CDIac With DabsCode Quality Standards (black, Isort, Mypy)

Preferred

Multi-country SchemasDrift/cost MonitoringDeployment AutomationAI Agents / Agentic Infrastructure

Stack & domain

PythonSQLDeltaUnity CatalogDatabricks JobsDatabricks Asset BundlesMlflowAzure DevopsGitflowPrsIacBlackIsortMypyData ProductsMachine LearningDemand PlanningBudget ForecastingData InfrastructureHigh-level Decision-makingCustom Ai SolutionsCloud-based SystemsGenerative AiE-commerceRetailVisual RecognitionAutomationProduct CategorizationQuality ControlInformation RetrievalRecommendation ModelsUser ExperienceEcommerceStreamingDigital PlatformsEngagementConversions

About the role

Original posting from Muttdata via Lever

🚀 Join Our Remote Data Products & Machine Learning Startup! 🚀

At Muttdata, we build innovative Data Products and Machine Learning solutions that help companies solve complex business challenges. As a fast-growing, remote-first startup, we're passionate about technology, collaboration, and continuous learning.

This opportunity is with a leading multinational beverage company based in Mexico City.

We are looking for a Machine Learning Engineer Senior to join our team 🐶🚀. You'll own the deployment, job configuration, and end-to-end operation of our AI Factory products, making sure the full pipeline — from data generation to publishing results to operations — runs reliably, observably, and scalably across countries.

This role works closely with development squads and platform teams, diagnosing runtime incidents and keeping production pipelines healthy across environments. Strong ownership, operational rigor, and end-to-end product knowledge are essential to succeed in this fast-paced, collaborative environment.

🚀 What We Do:

  • Leveraging our expertise, we build modern Machine Learning systems for demand planning and budget forecasting.
  • Developing scalable data infrastructures, we enhance high-level decision-making, tailored to each client.
  • Offering comprehensive Data Engineering and custom AI solutions, we optimize cloud-based systems.
  • Using Generative AI, we help e-commerce platforms and retailers create higher-quality ads, faster.
  • Building deep learning models, we enhance visual recognition and automation for various industries, improving product categorization, quality control, and information retrieval.
  • Developing recommendation models, we personalize user experiences in e-commerce, streaming, and digital platforms, driving engagement and conversions.

🌟 Our Partnerships:

  • Amazon Web Services
  • Astronomer
  • Databricks

🌟 Our Values:

  • 📊 We are Data Nerds
  • 🤗 We are Open Team Players
  • 🚀 We Take Ownership
  • 🌟 We Have a Positive Mindset

🔍 Curious about what we’re up to? Check out our case studies and dive into our blog post to learn more about our culture and the exciting projects we’re working on! 🚀

Responsibilities 🤓:

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

Required Skills 🚀:

  • Proven experience orchestrating data/ML pipelines on Databricks (Jobs, Workflows, cluster policies).
  • Solid CI/CD experience (Azure DevOps, gitflow, PRs), IaC with DABs, and code quality standards (black, isort, mypy).
  • Advanced Python, SQL, Delta / Unity Catalog.

Nice to Have 😉:

  • Experience with multi-country schemas, drift/cost monitoring, and deployment automation.
  • Experience developing AI agents / agentic infrastructure (e.g. Mosaic AI Agent Framework, agent orchestration, MCP).
  • Currently based in Mexico or a location within a similar time zone, allowing for strong overlap with Mexico working hours.

🎁 Perks:

  • Remote-first culture – work from anywhere! 🌍
  • AWS, DBT, Google Cloud, Azure & Databricks certifications fully covered
  • Birthday off + an extra vacation week (Mutt Week! 🏖️)
  • Referral bonuses – help us grow the team & get rewarded!
  • Maslow: Monthly credits to spend in our benefits marketplace.
  • ✈️🏝️ Annual Mutters' Trip – an unforgettable getaway with the team!

Source: Muttdata careers (Lever)

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