Job Summary
The Senior ML Engineer will be responsible for deploying machine learning models and building the Databricks-based MLOps, pipeline, and CI/CD infrastructure that supports enterprise AI/ML solutions. This role partners closely with data scientists, data engineers, and platform teams to productionize machine learning models through automated workflows, scalable data pipelines, model lifecycle management, and governed deployment processes. The ideal candidate combines strong software engineering expertise with hands-on experience in machine learning operations and cloud-based data platforms.
Key Responsibilities
• Build and operationalize machine learning pipelines within Databricks to support model training, validation, batch scoring, and deployment workflows.
• Design, develop, and maintain reusable ML workflows for data preparation, feature engineering, model training, evaluation, deployment, and inference.
• Implement and maintain CI/CD pipelines for machine learning code, data pipelines, and model promotion processes using Git-based development practices.
• Collaborate with data scientists and data engineers to transition experimental models into production-ready solutions.
• Build and manage feature engineering and data pipelines supporting model retraining, scoring, monitoring, and downstream consumption.
• Establish and maintain model lifecycle management processes using MLflow and Unity Catalog.
• Manage model registration, versioning, lineage, experiment tracking, and controlled promotion across environments.
• Develop automated testing, validation, and monitoring processes to improve reliability and reduce deployment risk.
• Manage Databricks jobs, workflows, orchestration processes, and scheduled executions.
• Package, version, and promote model artifacts with full traceability to source code, datasets, and registry versions.
• Collaborate with machine learning, data engineering, cloud platform, and business stakeholders to ensure scalable and supportable solutions.
• Support deployment patterns that enable operational use of AI/ML solutions in production environments.
• Contribute to the development of reusable MLOps frameworks and deployment standards across multiple AI/ML initiatives.
Required Qualifications
• Bachelor's degree, Master's degree, or equivalent experience in Computer Science, Data Science, Engineering, or a related technical field.
• Strong software engineering experience using Python.
• Experience deploying machine learning models into production environments.
• Strong experience with Databricks, including Jobs, Workflows, Repos, and MLflow.
• Experience implementing MLflow-based model lifecycle management processes.
• Experience building CI/CD pipelines for machine learning or data products.
• Experience using Git-based development workflows and automated testing frameworks.
• Strong understanding of data pipelines, feature engineering, batch processing, and workflow orchestration.
• Experience supporting model development, deployment, and operational management.
• Strong analytical, troubleshooting, and problem-solving skills.
• Ability to collaborate effectively across cross-functional technical teams.
Preferred Qualifications
• Experience supporting manufacturing, industrial IoT, quality engineering, or plant-floor analytics initiatives.
• Experience with model governance, lineage tracking, reproducibility, and controlled model promotion processes.
• Experience designing resilient machine learning pipelines capable of handling evolving data conditions and retraining requirements.
• Familiarity with model monitoring, operational observability, and validation frameworks.
• Experience transitioning proof-of-concept or research-based models into production-ready solutions.
• Experience building enterprise-scale MLOps platforms and deployment frameworks.
Primary Skills
• Python
• Databricks
• MLflow
• MLOps
• CI/CD Pipelines
• Git
• Data Pipelines
• Feature Engineering
• Machine Learning Deployment
• Workflow Orchestration
• Unity Catalog
