Machine Learning Developer

Job Responsibilities

• Establish the department’s MLOps standards, reusable pipeline patterns, and “golden path” for taking a model from notebook to production
• Partner with data science teams to productionize models using Databricks MLflow, AutoML, Unity Catalog, and Model Serving
• Design and maintain automated CI/CD pipelines for model training, deployment, and controlled promotion across environments
• Govern the model lifecycle through experiment tracking, model registration, versioning, lineage, and access control
• Establish model and data monitoring, validation checks, and operational observability; support incident response and reliability of production ML systems
• Enforce data and feature quality, schema validation, and data versioning so models train and infer on trusted inputs
• Author documentation, reference architectures, and playbooks; lead code reviews and knowledge-sharing to drive consistent engineering practice
• Cookordination with business stakeholders, data scientists, data engineers, and IT to define requirements and drive adoption of shared frameworks
• Evaluate emerging tools and patterns, including agentic and LLM-assisted development workflows, and recommend improvements to ML delivery

Required Qualifications

• Bachelor’s Degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or related field
• Three (3) to five (5) years of hands-on experience building, deploying, and operating machine learning or data-intensive systems in production
• Strong proficiency in Python as a primary engineering language, with experience writing tested, maintainable production code
• Strong SQL skills and working knowledge of Spark or other distributed data processing frameworks
• Practical experience establishing or operating an MLOps workflow, including model deployment, pipeline automation, monitoring, and lifecycle management
• Software engineering fundamentals including version control (Git), unit testing, CI/CD, and common design patterns
• Able to explain the intuition behind common ML algorithms and follow model training, evaluation, and hyperparameter tuning best practices
• Strong interpersonal, analytical, and communication skills, with the ability to work effectively across data science, engineering, and business teams

Preferred Qualifications

• Experience with Unity Catalog for model governance, lineage, and controlled promotion of ML assets
• Databricks certification (e.g., Databricks Certified Machine Learning Associate or Professional)
• Master’s Degree in a related field
• Familiarity with cloud data platforms, infrastructure-as-code, containerization and orchestration
• Exposure to LLM/GenAI application patterns such as RAG and evaluation harnesses, and to agentic or AI-assisted development workflows
• Experience mentoring or training data scientists on engineering best practices
• Ability to operate both independently and as part of a team
• Self-starter requiring minimal supervision with strong organizational and time management skills