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About This Role
Job descriptionCompany and benefits
Job IDFELLO017822
Employment TypeRegular
Work Styleremote
LocationUnited States, Seattle,WA,United States, San Francisco,CA,United States
TravelUp to 25%
RoleFellow Software Engineer\-Eng (Agentic AI Architecture \& Engineering)Why UKG:
At UKG, the work you do matters. The code you ship, the decisions you make, and the care you show a customer all add up to real impact. Today, tens of millions of workers start and end their days with our workforce operating platform. Helping people get paid, grow in their careers, and shape the future of their industries. That’s what we do.
We never stop learning. We never stop challenging the norm. We push for better, and we celebrate the wins along the way. Here, you’ll get flexibility that’s real, benefits you can count on, and a team that succeeds together. Because at UKG, your work matters—and so do you.
About the Role:
We are seeking a distinguished and customer\-facing Fellow AI Engineer to define and lead UKG’s enterprise\-wide Agentic AI strategy across the full UKG Product Suite. This role extends beyond technical excellence—it requires a seasoned AI leader who partners closely with customers, sales leaders, product teams, and engineering experts to translate AI innovation into measurable business impact.
As a Technical Fellow, you will serve as the strategic and architectural authority for Agentic AI systems. You will shape how autonomous, goal\-driven AI agents are designed, deployed, governed, and scaled across all the complete UKG product suite. This role demands a rare blend of deep technical mastery, enterprise architecture thinking, commercial awareness, and executive\-level influence and communication.
You will operate at the intersection of customer outcomes, product innovation, and engineering excellence—ensuring UKG leads the market in responsible, scalable, and transformative Agentic AI solutions.
\*\*\*UKG is unable to offer sponsorship for this position.\*\*\*
Responsibilities:
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
Engage directly with strategic customers, sales teams, and solution consultants to:
- Shape AI\-driven value propositions
- Identify high\-impact agentic use cases
- Support executive conversations and AI strategy discussions
- Translate real\-world customer challenges into scalable AI platform capabilities
Serve as a trusted AI advisor in customer engagements, industry forums, and strategic deal cycles.
Cross\-Functional Leadership
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
Provide hands\-on architectural leadership in:
- Multi\-agent systems and orchestration frameworks
- LLM\-powered agents and tool\-use architectures
- Retrieval\-augmented generation (RAG) and knowledge systems
- 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
- Risk management and compliance
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.
Qualifications:
- 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.
Preferred Qualifications:
- 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).
Company Overview:
UKG is the Workforce Operating Platform that puts workforce understanding to work. With the world's largest collection of workforce insights, and people\-first AI, our ability to reveal unseen ways to build trust, amplify productivity, and empower talent, is unmatched. It's this expertise that equips our customers with the intelligence to solve any challenge in any industry — because great organizations know their workforce is their competitive edge. Learn more at ukg.com.
Equal Opportunity Employer
UKG is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, disability, religion, sex, age, national origin, veteran status, genetic information, and other legally protected categories.
View The EEO Know Your Rights poster
UKG participates in E\-Verify.
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Disability Accommodation in the Application and Interview Process
For individuals with disabilities that need additional assistance at any point in the application and interview process, please email UKGCareers@ukg.com.
The pay range for this position is $233,300\.00 to $335,400\.00 USD. The actual base pay offered may vary depending on skills, experience, job\-related knowledge and work location. In addition to base pay, employees may be eligible to participate in a performance\-based bonus plan and to receive restricted stock unit awards as part of total compensation. Learn more about UKG’s benefits and rewards at https://www.ukg.com/about\-us/careers/benefits
NOTICE ON HIRING SCAMS
UKG will never ask you for a copy of your driver’s license, social security card, or passport during a job inter
ABOUT OUR JOB DESCRIPTIONS
All job descriptions are written to accurately reflect the open job and include general work responsibilities. They do not present a comprehensive, detailed inventory of all duties, responsibilities, and qualifications required for the job. Management reserves the right to revise the job or require that other or different tasks be performed if or when circumstances change.
Salary Context
This $233K-$335K range is above the 75th percentile for AI Architect roles in our dataset (median: $225K across 99 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 26,159 AI roles we're tracking, AI Architect positions make up 1% of the market. At UKG, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $292,900 based on 108 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. Disclosed range: $233K to $335K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
UKG AI Hiring
UKG has 3 open AI roles right now. They're hiring across Data Scientist, AI Architect, AI Product Manager. Positions span Weston, FL, US, San Francisco, CA, US, Lowell, MA, US. Compensation range: $186K - $335K.
Location Context
AI roles in San Francisco pay a median of $244,000 across 1,059 tracked positions. That's 33% above the national median.
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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