Principal AI Engineer

UTA seeks a Principal AI Engineer to lead the design, development, and delivery of intelligent software systems that bring together enterprise application architecture, full-stack engineering, data science, and machine learning. In this highly visible, hands-on leadership role, you will build and scale production-grade platforms and AI-enabled products that power critical workflows, decision-making, automation, and business insight across the organization. The ideal candidate combines deep software engineering expertise with strong applied AI and data science capability, and has a proven ability to mentor engineers, shape technical direction, and partner cross-functionally to turn complex business problems into scalable, secure, and reliable solutions. What You Will Do AI Engineering, Data Science & Intelligent Product Development - Lead the design, development, and deployment of machine learning, statistical, and AI-driven solutions that support automation, prediction, decision-making, and personalization. - Own end-to-end delivery of AI-enabled products and services, from problem framing and data exploration through model development, application integration, deployment, and monitoring. - Evaluate and implement appropriate approaches across machine learning, analytics, and natural language processing based on business needs and technical constraints. - Partner with data and platform teams to build robust pipelines, reusable services, and scalable environments that support production AI workloads. Platform Engineering & Enterprise Architecture - Design and develop scalable, secure, cloud-native applications and backend services that integrate AI, data, and business workflows into enterprise platforms. - Lead end-to-end full-stack engineering across backend services and modern web applications, including APIs, data models, service integrations, and internal tools. - Architect systems using modern cloud patterns such as microservices, event-driven design, and managed services, ensuring reliability, observability, and scalability. - Provide architectural leadership across integrations with enterprise systems and third-party platforms. ML Ops, Reliability & Engineering Best Practices - Productionize AI and machine learning solutions using modern ML Ops and software engineering practices. - Establish standards for testing, deployment, observability, drift detection, retraining, and documentation. - Drive quality, automation, and performance in systems where accuracy, resilience, and reliability are critical. Leadership, Mentorship & Execution - Serve as a hands-on technical leader and player-coach, mentoring engineers while actively contributing to design and implementation. - Help define technical strategy and roadmap priorities for AI, data, and application development. - Lead execution of complex, cross-functional initiatives and act as a senior escalation point for technical decisions and trade-offs. - Foster a culture of engineering excellence, accountability, and continuous improvement. Collaboration & Business Partnership - Partner with Product, Engineering, Data, and business stakeholders to identify high-value opportunities where AI and software can materially improve outcomes. - Translate ambiguous business needs into clear technical solutions, communicating trade-offs and risks effectively. - Present complex technical work in a clear, actionable way to both technical and non-technical audiences, including leadership. What You Will Need - Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field, or equivalent experience; advanced degree is a plus. - 10+ years of experience in software engineering, applied machine learning, data science, or related fields, including building and delivering production systems end-to-end. - Strong hands-on expertise in modern software engineering, including backend development, APIs, and scalable system design. - Experience with full-stack development, including…