ML Engineer Deployment and Databricks MLOps

We are looking for a hands-on Senior ML Engineer to help productionize machine learning solutions for manufacturing use cases involving deployment and pipeline buildout. This role will sit at the intersection of model operationalization, data/feature pipelines, and CI/CD, helping us move from proof of concept to repeatable, governed, production-ready delivery. The role is aligned to our current direction of hardening Databricks-based MLOps infrastructure, MLflow-based lifecycle management, and CI/CD-driven promotion of models and workflows into operational use.

What this role will do

• Build and operationalize ML pipelines in Databricks to support training, validation, batch scoring, and deployment workflows.
• Implement and maintain CI/CD pipelines for ML code, data pipelines, and model promotion using Git-driven development practices and automated quality checks.
• Partner with data scientists and data engineers to turn experimental models into production candidates with clear dependencies, reproducible artifacts, and governed deployment paths.
• Build and manage feature/data pipelines that support model retraining, re-scoring, monitoring, and downstream consumption.
• Establish model lifecycle controls using MLflow and Unity Catalog, including experiment tracking, model registration, versioning, lineage, and controlled promotion across environments.
• Improve reliability of ML systems through data validation, testing, monitoring, and automation that reduce manual intervention and deployment risk.
• Support deployment patterns that can extend from lab and cloud development into plant-ready operational workflows over time., * Productionize machine learning models developed by the data science team for manufacturing applications.
• Design, build, and maintain reusable ML workflows for data preparation, feature engineering, model training, evaluation, deployment, and inference.
• Own CI/CD patterns for ML and pipeline assets, including unit tests, smoke tests, code quality checks, and release automation.
• Manage Databricks jobs and workflows for retraining, scoring, orchestration, and scheduled execution.
• Package and promote versioned model artifacts with traceability to code commits, data snapshots, and registry versions.
• Collaborate across ML, data engineering, cloud/platform, and manufacturing stakeholders to ensure deployed solutions are scalable, supportable, and aligned to production constraints., * In the near term, this person will help establish a repeatable path to deploy and operate manufacturing ML solutions on Databricks, including model lifecycle management, underlying pipelines, and CI/CD automation. Over time, the role should help create a reusable template that bridges experimentation, deployment, and governed operations across multiple manufacturing AI/ML use cases.
• Short posting version
• We are seeking a Senior ML Engineer to help deploy machine learning models and build the Databricks-based MLOps, pipeline, and CI/CD foundation behind them. This person will partner with data scientists, data engineers, and platform teams to productionize manufacturing AI/ML solutions through MLflow-based model lifecycle management, automated workflows, governed releases, and scalable data/feature pipelines.

• Bachelor s, Master s, or equivalent experience in Computer Science, Data Science, Engineering, or a related technical field.
• Strong software engineering skills in Python and production-quality development practices.
• Experience deploying machine learning models into production environments.
• Strong experience with Databricks, including jobs/workflows, repos, and MLflow-based experimentation and model lifecycle management.
• Experience building CI/CD pipelines for ML or data products using Git-based workflows and automated testing.
• Strong understanding of data pipelines, feature engineering, batch processing, and pipeline orchestration.
• Experience working across model development, deployment, and operational support in cross-functional environments., * Experience with manufacturing, industrial IoT, quality, or plant-floor analytics use cases.
• Experience with model governance, lineage, reproducibility, and controlled promotion of ML assets across environments.
• Experience designing resilient ML pipelines that can handle changing upstream data conditions and retraining needs.
• Familiarity with model monitoring, validation checks, and operational observability.
• Experience supporting the transition from PoC or R&D models into production-ready solution patterns.