Senior ML Engineer - Remote Data Pipelines & Production AI

Openkrill; United States; Remote; USD 150,000 - 190,000; Full time

Benefits offered by this job

Remote-first culture

Certifications covered

Birthday off + extra vacation week

Referral bonuses

Monthly benefits marketplace credits

Annual company trip

Job summary

Muttdata is a remote-first, fast-paced startup building data products and ML solutions. We seek a Senior Machine Learning Engineer to own end-to-end deployment, job configuration, and production pipelines across countries, ensuring reliability and observability of AI factories.

You will work closely with development squads and platform teams to diagnose runtime incidents, promote models, and maintain scalable, low-friction ML workflows in a distributed environment.

Qualifications

• Proven experience orchestrating data/ML pipelines on Databricks (Jobs, Workflows).
• Solid CI/CD experience and IaC with DABs, plus code quality standards.
• Advanced Python, SQL, Delta / Unity Catalog.

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.

Skills

Databricks pipelines

Python

SQL

CI/CD

Git workflows

Tools

Databricks (DABs)

MLflow

Unity Catalog

Azure DevOps

gitflow

Job description

Muttdata is a remote-first, fast-paced startup building data products and ML solutions. We seek a Senior Machine Learning Engineer to own end-to-end deployment, job configuration, and production pipelines across countries, ensuring reliability and observability of AI factories.

You will work closely with development squads and platform teams to diagnose runtime incidents, promote models, and maintain scalable, low-friction ML workflows in a distributed environment.