Lead AI/ML Engineer

Job DescriptionAbout Kendra ScottWe are a fashion-lifestyle brand of big dreams, colorful confidence, and inspired design. Founded on Family, Fashion and Philanthropy, we believe that our employees and customers are the heart and soul of our brand, and that giving back is the truest form of success. Fashion meets innovation in the design of our two state-of-the-art facilities based in the heart of Austin, Texas, and overflows into the design of our growing retail environment across the country.  Position OverviewKendra Scott is seeking a Lead AI/ML Engineer to advance how we use artificial intelligence to improve business performance and reduce manual work. Your primary focus will be partnering with business teams to design and deliver AI-enabled workflows. You will also maintain existing machine learning models and conduct prioritized advanced analysis to support business decisions. As a hands-on technical lead, you will guide solutions from discovery through production support, working with shared data engineering, application development, DevOps and QA resources. You will help establish our AI engineering practices while building solutions directly. The Vice President, Digital Technology owns program direction and prioritization. Functional process owners remain accountable for business process decisions, acceptance and adoption. This role provides technical input into governance reviews and implements approved requirements. ResponsibilitiesBuild AI-enabled workflows and applications Partner with business process owners to understand current workflows, identify automation opportunities and establish measurable outcomes. Determine when to use AI, conventional automation or existing enterprise capabilities. Design and build workflows that connect enterprise applications and data through APIs and other integration methods. Develop AI assistants and agents that retrieve information, support decisions and execute approved business actions. Incorporate human review and approval where business judgment or risk requires it. Work with business owners to validate solutions, support adoption and measure realized benefits. Maintain deployed solutions and improve them based on operating performance and user feedback. Deliver applied data science and machine learning Translate prioritized business questions into analytical approaches and predictive modeling opportunities. Conduct advanced analysis to explain business performance and recommend actions. Develop and validate machine learning models using appropriate statistical methods. Maintain existing models, monitor performance and address changes in accuracy or business relevance. Communicate findings, assumptions and model limitations in language business partners can use. Partner with data engineering to ensure models and AI applications use reliable data. Establish production controls and support practices Evaluate AI outputs and workflow behavior against defined acceptance criteria before deployment. Implement approved access permissions, including limits on the actions automated workflows can perform. Establish audit logs, monitoring and exception handling. Design workflows so they can be stopped safely, with recovery or reversal procedures where feasible. Apply data protection requirements in partnership with Information Security and designated governance owners. Coordinate deployment and production support with shared technical resources, including clear escalation paths and backup coverage. Monitor solution operating costs and recommend changes that improve value. Build a sustainable delivery capability Establish reusable approaches for development, testing and documentation. Maintain code in shared repositories with version control and appropriate review practices. Document solution dependencies, operating procedures and known limitations. Transfer knowledge to designated backup resources so production support does not depend on one person. Work with PMO and business owners to estimate effort and manage delivery through a prioritized backlog. Provide technical input into AI technology evaluations and governance reviews. Our Ideal Candidate Will HaveTypically five or more years of relevant experience in applied data science, machine learning engineering or software engineering, with ownership of production solutions. Demonstrated production experience in both predictive modeling and AI-enabled application or workflow development. A strong foundation in statistics, model validation and interpretation of business results. Strong Python and SQL skills. Hands-on experience building applications with large language models and evaluating their outputs. Experience integrating enterprise systems through APIs and working with cloud services. Experience deploying and supporting production applications or models, including automated testing and monitoring. Ability to design appropriate controls for unreliable AI outputs and automated business actions. Ability to work directly with business partners, clarify requirements and explain technical tradeoffs plainly. Sound judgment about when to build a custom solution and when an existing capability meets the need. Ability to prioritize competing demands and communicate capacity constraints. Bachelor’s degree in a relevant field or equivalent practical experience. Experience that would be valuable Retail or ecommerce experience, particularly in customer analytics, merchandising, marketing or business operations. Experience with AWS and Snowflake. Experience with workflow automation platforms or enterprise integration tools. Experience building applications that retrieve information from enterprise sources to inform AI responses. Familiarity with Microsoft 365, Salesforce or Zendesk integrations. Experience measuring automation benefits, including time saved and improvements in process quality. We are an equal opportunity employer and value diversity at our company.DivisionHome Office LocationCorporate Office Employees Position TypeFull Time Zip Code78756CityAustinStateTexas Workplace TypeOn-site Organizational LevelIC4- Senior II : INFORMATION TECHNOLOGY