Machine Learning Engineer

AI/ML Engineer - Databricks

Position Summary

We are seeking an experienced AI/ML Engineer with strong hands-on Databricks experience to develop and deploy data and machine learning solutions supporting manufacturing operations. The ideal candidate will have a strong background in Databricks, PySpark, SQL, MLflow, Python, and production ML workflows, with the ability to work with large-scale datasets and build scalable solutions.

The ideal candidate will be someone who has recent, hands-on production experience with Databricks, rather than someone who has only listed Databricks as a skill. Candidates should be able to clearly explain the Databricks projects they have worked on, their role in building the solution, the technologies they used, and how the solution was deployed and supported in production.

Key Responsibilities

Design, develop, and optimize data and ML pipelines using Databricks.

Develop scalable data processing solutions using PySpark and Spark SQL.

Build and manage data pipelines using Delta Lake.

Develop, train, and deploy machine learning models using Databricks and MLflow.

Perform data preparation, transformation, feature engineering, and analysis for ML applications.

Optimize Databricks and Spark workloads for performance and scalability.

Implement production-ready ML workflows, including model tracking, deployment, and monitoring.

Work with manufacturing, engineering, quality, and IT teams to translate business problems into data and ML solutions.

Support AI/ML applications related to manufacturing, quality, process optimization, predictive maintenance, or other production use cases.

Required Qualifications

5+ years of experience in Data Engineering, Machine Learning Engineering, AI/ML, or a related field.

3+ years of strong hands-on experience with Databricks in a production environment.

Strong hands-on experience with Databricks, PySpark/Apache Spark, Spark SQL, Delta Lake, MLflow, Python, and SQL.

Experience building and deploying production data pipelines and ML workflows.

Strong understanding of data processing, transformation, feature engineering, and performance optimization.

Experience working with cloud platforms such as AWS, Azure, or GCP.

Experience with machine learning model development, training, deployment, and monitoring.

Strong communication, analytical, and problem-solving skills.

Preferred Qualifications

Experience applying AI/ML in manufacturing, automotive, industrial, or production environments.

Experience with machine vision, predictive maintenance, quality analytics, or manufacturing data.

Experience with MLOps and production ML deployments.

Experience with large-scale data platforms and cloud-based ML infrastructure.

Experience working with manufacturing engineers, quality teams, plant operations, or other technical stakeholders.