Job Description
Agentic AI Vision & Enterprise Strategy Define and drive UKG's enterprise-wide Agentic AI strategy, establishing a cohesive architectural and execution framework for intelligent agents across the product portfolio and AI Platform. Translate emerging AI capabilities into a clear roadmap aligned with business priorities, customer value, and long-term platform evolution. Customer & Field Partnership Ensure AI systems align with UKG's commitment to trust, fairness, and ethical innovation. Mentorship & Organizational Impact Elevate AI maturity across the organization by mentoring senior engineers and architects. Foster a culture of pragmatic innovation--balancing bold experimentation with disciplined execution. Industry Representation Represent UKG as a thought leader in Agentic AI through publications, keynote speaking, customer roundtables, and industry collaborations. Collaborate with Product, Engineering, Data Science, Security, and Legal teams to ensure alignment on architecture, governance, and responsible AI frameworks. Facilitate cross-functional consensus to operationalize AI agents in a manner that is secure, compliant, and ready for production deployment. Lead discussions on AI technology at the UKG Board level. Technical Authority & Architecture Multi-agent systems and orchestration frameworks LLM-powered agents and tool-use architectures Autonomous workflow execution and decision intelligence AI safety, evaluation, and guardrails Establish standards for scalability, observability, cost optimization, and performance across cloud-native deployments. Innovation & Applied Research Stay at the forefront of Agentic AI advancements, continuously evaluating emerging models, frameworks, and research. Lead rapid experimentation and incubation of next-generation AI capabilities, bridging cutting-edge research with production-grade systems. Commercial & Product Influence Collaborate with GTM and Product leadership to ensure AI investments translate into differentiated product offerings. Influence pricing models, packaging strategies, and AI monetization approaches aligned with enterprise customer expectations. Responsible AI & Governance Define and champion governance frameworks for autonomous AI systems, including: Transparency and explainability Human-in-the-loop controls Privacy-preserving architectures Educational Background: PhD in Computer Science, AI, Machine Learning, or a related field, or equivalent industry experience. Experience: 15+ years in software development and AI, with at least 5 years of hands-on experience in generative AI, NLP, or related fields. Proven expertise in architecting and deploying large-scale AI/ML systems in production environments. Track record of influencing cross-functional stakeholders, including customer-facing teams Technical Proficiency: Expert-level skills in programming languages (e.g., Python, Java) and AI frameworks (e.g., TensorFlow, PyTorch). Strong understanding of cloud platforms (AWS, Google Cloud, Azure) and MLOps practices for large-scale model training and deployment. AI Methodologies: In-depth knowledge of generative AI methodologies, including transformer models, diffusion models, GANs, large language models, and multi-modal architectures. Familiarity with NLP and machine learning algorithms, such as linear and logistic regression, decision trees, and clustering methods. Experience partnering with sales and customers to shape AI roadmaps Industry Influence: Recognized thought leader in AI, with a record of publications in top-tier AI conferences/journals (e.g., NeurIPS, ICML, CVPR) and a strong network within the AI research community. Executive presence with strong communication and storytelling skills Problem-Solving & Strategy: Exceptional problem-solving skills and a proven ability to influence and implement long-term AI-driven strategic initiatives. Compliance & Responsible AI: Experience working in high-compliance environments or with privacy-preserving AI techniques. Strong familiarity with trends in responsible AI, model interpretability, and ethical AI practices. Optimization Expertise: Proven record of optimizing AI models for cost-efficiency at scale through model compression, distillation, and efficient deployment strategies. Cloud & DevOps Knowledge: Strong experience with cloud-native architectures, containerization (e.g., Kubernetes), and CI/CD pipeline automation (e.g., Terraform, GitHub Actions).
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