Principal Software Engineer (AI/ML Architect)

Architectural Vision & Strategy: Define 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 Leadership: Serve 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 Collaboration: Work 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 & Research: Conduct 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 Building: Mentor 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 Engagement: Act as an ambassador for generative AI internally and externally, representing UKG in the AI community through publications, speaking engagements, and industry forums. Scalable Solutions: Oversee 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 & Standards: Define 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 Background: MS 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 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. 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. 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).