Principal Software Engineer (AI/ML Architect)

Architectural Vision & StrategyDefine and drive the generative AI architecture strategy, ensuring UKG remains at the leading edge of AI innovation. Develop and communicate a cohesive architectural vision that aligns with business goals, enabling the seamless integration of GenAI capabilities across our product suite. Technical LeadershipServe as the primary technical visionary for generative AI, providing hands-on guidance in advanced methods (e.g., transformer models, diffusion models, GANs) and setting technical standards that ensure scalability, security, and efficiency. Cross-Functional CollaborationWork closely with executive leadership, product management, data science, and engineering teams to establish and prioritize GenAI initiatives. Collaborate with cross-functional teams to ensure alignment on requirements and objectives, driving the infusion of AI capabilities across products. Innovation & ResearchConduct hands‑on, advanced research in generative AI, staying current with emerging technologies, industry trends, and best practices. Lead the exploration and implementation of state‑of‑the‑art GenAI techniques to enhance product value and drive a competitive edge. Mentorship & Culture BuildingMentor and influence senior engineering leaders, fostering a culture of AI excellence, thought leadership, and continuous innovation. Champion best practices in AI/ML development, MLOps, CI/CD processes, and quality assurance to ensure high standards across the organization. Community EngagementAct as an ambassador for generative AI internally and externally, representing UKG in the AI community through publications, speaking engagements, and industry forums. Scalable SolutionsOversee the deployment of large-scale AI models, ensuring they are optimized for performance, cost, and resource efficiency in production environments. Establish guidelines for high‑quality, production‑ready AI/ML systems that can scale with business needs. Governance & StandardsDefine and enforce development methodologies, CI/CD standards, and architectural guidelines for AI solutions. Maintain documentation of architectural decisions and technical roadmaps, ensuring a sustainable foundation for future AI‑driven capabilities. Educational BackgroundMS or PhD in Computer Science, AI, Machine Learning, or a related field, or equivalent industry experience. Experience 12+ 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. Technical ProficiencyExpert‑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 MethodologiesIn‑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. Industry InfluenceRecognized 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. Problem‑Solving & StrategyExceptional problem‑solving skills and a proven ability to influence and implement long‑term AI‑driven strategic initiatives. Compliance & Responsible AIExperience 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 ExpertiseProven record of optimizing AI models for cost‑efficiency at scale through model compression, distillation, and efficient deployment strategies. Cloud & DevOps KnowledgeStrong experience with cloud‑native architectures, containerization (e.g., Kubernetes), and CI/CD pipeline automation (e.g., Terraform, GitHub Actions).