Principal Software Engineer (AI/ML Architect)

UKG; Seattle (WA); On-site; USD 180,000 - 240,000; Full time

Job summary

UKG is seeking a senior Architectural Leader to define and drive the generative AI vision across our product suite. You will guide advanced GenAI methods, set standards for scalability and security, and collaborate with executives to prioritize AI initiatives.

You will mentor engineering leaders, champion MLOps and CI/CD practices, and help UKG maintain a competitive edge through production-ready AI deployments.

Qualifications

• Advanced degree or equivalent in CS/AI with strong GenAI background.
• Proven ability to architect scalable AI systems for production.
• Hands-on experience with transformer, diffusion, GANs and LLMs.
• Experience delivering AI solutions with MLOps and CI/CD.

Responsibilities

• Define and drive generative AI architecture strategy aligned with business goals.
• Provide hands-on guidance on advanced GenAI methods and standards.
• Collaborate with leadership, product, data science and engineering teams.
• Lead research on state-of-the-art GenAI techniques for product value.
• Mentor senior engineers, promote AI excellence and best practices.

Skills

GenAI

Architectural leadership

MLOps

Cloud platforms

Education

MS or PhD in CS

Tools

TensorFlow

PyTorch

AWS

Google Cloud

Azure

Job description

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).