Note: The job is a remote job and is open to candidates in USA. White Cap is a company committed to Building Trust on Every Job through knowledgeable, capable, and dependable associates. The Principal ML/AI Engineer serves as the Data Science team's senior technical leader, designing and operationalizing enterprise machine learning and artificial intelligence solutions while setting architectural and engineering standards. The role provides hands-on technical leadership, mentorship, cross-functional partnership, and guidance on scalable, secure, reliable, and maintainable production systems.
Responsibilities
- Serves as the principal engineering authority for enterprise-grade systems supporting data science, machine learning, and AI in Azure and Databricks. Sets the technical direction and architectural patterns that turn analytical models and prototypes into production solutions that are scalable, secure, reliable, and maintainable
- Leads the architecture and hands-on development of applications, services, APIs, and reusable components that deliver and operate data science solutions, covering model execution, business workflows, user experiences, system integrations, and downstream consumption of model outputs
- Designs systems to scale across users, datasets, models, and business processes. Owns the tradeoffs related to performance, fault tolerance, availability, security, maintainability, and cost, and surfaces architectural risks early so solutions support long-term enterprise use
- Establishes the engineering practices for deploying, operating, and monitoring ML/AI solutions in production. Defines standards for CI/CD, automated testing, model and code versioning, environment management, observability, rollback, and production support across the full solution lifecycle
- Defines and enforces software engineering standards for teams building systems in support of data science
- Leads architecture, design, and code reviews covering code quality, modularity, testing, documentation, security, and maintainability, and serves as the final escalation point for complex technical decisions
- Mentors engineers supporting data science and AI initiatives and guides them through complex implementation challenges, promotes engineering best practices, and builds the team's depth in cloud architecture, software development, distributed systems, and MLOps
- Partners with data scientists, data engineers, product teams, enterprise architects, information security, infrastructure teams, and business stakeholders to ensure data science solutions are built on appropriate software and platform architectures
- Translates analytical requirements into engineering designs and communicates technical decisions, dependencies, risks, and tradeoffs clearly
Skills
- Typically requires a bachelor's degree and 10+ years of experience in a related field OR MS/MA and generally 8+ years of experience in a related field. Maintains expert knowledge in area of responsibility with a strong understanding in adjacent areas for the development of creative solutions
- Typically requires overnight travel less than 10% of the time
- Advanced degree in computer science
- Strong ability to summarize and communicate technical challenges and solutions effectively
Benefits
- Remote work arrangement
- Mentorship and support for engineers
- Career opportunities
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