Sr. ML Engineer

Senior ML Engineer
Interview on-site in Austin, TX.
Candidates must be able to interview on-site in Austin, TX.
This role sill start as a 3 month contract but has a strong chance of extension.
The interview process will be 1 on-site interview and then an offer will be made.
The Hiring Manager is looking to fill this role ASAP.

This is what the primary responsibility of this role will consist of:-

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.

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Senior ML Engineer - Deployment and Databricks MLOps
Role summary
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.

Key responsibilities

• 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.

Required qualifications

• 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.

Preferred qualifications

• 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.

What success looks like
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.