Data & AI Product Engineer

Salas O’Brien is an employee-owned engineering and professional services firm focused on delivering lasting solutions across diverse industries. The Data & AI Product Engineer will build and support data pipelines, machine learning and generative AI components, and product applications from requirements through production while collaborating with product teammates and business users. The role also includes monitoring, testing, documentation, code reviews, defect resolution, and contributing to technical decisions.

Responsibilities:

• Build and operate data pipelines on the Lakehouse, including ingestion, transformation, quality checks, and the monitoring that catches failures early
• Develop product features from requirements through production, including testing and documentation as part of the delivery process
• Build machine learning or generative AI components when they support the product roadmap, including evaluation practices to confirm performance and usefulness
• Follow platform patterns and engineering standards for the pod, and share feedback when standards can be improved
• Participate in code reviews to improve quality, share knowledge, and support consistent engineering practices
• Support what you build by investigating defects, addressing root causes, and communicating updates clearly to the Product Manager
• Add instrumentation so product usage, reliability, and performance can be measured and improved
• Work directly with business users to clarify requirements, understand intent, and shape practical solutions
• Contribute to design discussions and technical decisions with the product pod
• Maintain clear documentation for the components and workflows you support

Requirements:

• Three or more years of professional experience in data engineering, machine learning engineering, analytics engineering, or software development
• Working Python and SQL skills, including the ability to write, test, and debug production code without close supervision
• Experience with a cloud data platform and distributed data processing concepts
• Experience with version control and collaborative development practices, including branching, pull requests, and code review
• Understanding of data modeling and data quality fundamentals
• Ability to work from a backlog, communicate progress clearly, and raise risks or timing considerations early
• Bachelor's degree in engineering, computer science, mathematics, or a related discipline, or equivalent experience
• Databricks certification at the associate or professional level
• Experience with MLflow, model serving, or generative AI application patterns
• Front-end development experience, including React or a comparable framework
• Familiarity with architecture, engineering, or professional services operations
• Hands-on Databricks experience, including workflows, Delta Lake, and Unity Catalog, is highly valued
• Experience with continuous integration and deployment practices for data workloads is also helpful
• Experience delivering solutions for internal business users is beneficial, especially when requirements begin as process needs or business questions rather than detailed technical specifications