Python Ml Developer Jobs in Leander, TX (NOW HIRING)

Senior ML Engineer – Deployment and Databricks MLOps

Location: Austin, TX/Onsite 4-5days per week

Job Type- Contract 

Job Description:

3 Month Contract - Can be extended- requires on-site work in Austin, TX 4-5 days per week

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.

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.

Regards,

Himanshu Rawat

himanshu@carmansg.com