Senior Machine Learning Engineer - Databricks - #2

Muttdata

31d•Remote

About The Position

Join Muttdata, a remote-first startup building innovative Data Products and Machine Learning solutions. This role is with a leading multinational beverage company based in Mexico City. As a Senior Machine Learning Engineer, you will own the deployment, job configuration, and end-to-end operation of AI Factory products, ensuring reliable, observable, and scalable pipelines from data generation to publishing results. You will collaborate closely with development squads and platform teams, diagnosing runtime incidents and maintaining production pipelines across environments. Strong ownership, operational rigor, and end-to-end product knowledge are key to success in this fast-paced, collaborative environment. Muttdata leverages expertise to build modern Machine Learning systems for demand planning and budget forecasting, develop scalable data infrastructures, offer comprehensive Data Engineering and custom AI solutions, utilize Generative AI for e-commerce, build deep learning models for visual recognition, and develop recommendation models for personalization.

Requirements

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 Haves

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.

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.