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About This Role
Job description
Company and benefits
Job ID
STAFF019373
Employment Type
Regular
Work Style
hybrid
Location
Seattle,WA,United States
Role
Staff Software Engineer (Agentic AI)
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.
\*\*\*UKG is unable to offer sponsorship for this position.\*\*\*
Staff Software Engineer \- Agentic Acceleration Group
We are seeking a highly experienced Staff Software Engineer to build and scale our agentic AI capabilities on Google's Agent Development Kit (ADK) and other enterprise agentic platforms. This role will provide you with the opportunity to work on cutting\-edge agentic AI systems, intelligent automation workflows, and impactful projects that are used by enterprises and users worldwide. You will drive technical strategy for agent\-powered features, lead large\-scale agentic AI projects, and mentor engineers across the organization in modern agent development practices. As a Staff Software Engineer, you will be responsible for the design, development, testing, deployment, and maintenance of highly complex agentic systems built on established AI platforms.
Responsibilities:
- Agent Development: Design, build, and deploy production\-grade AI agents using Google ADK and other agentic platforms (Anthropic Claude SDK, LangGraph, etc.). Write clean, maintainable, and efficient code for agent workflows, tool integrations, and multi\-agent orchestration.
- Platform Integration: Build agents that integrate with enterprise systems through tools, APIs, and Model Context Protocol (MCP) servers. Design and implement custom tools, skills, and plugins that extend agent capabilities across UKG's HCM platform.
- Technical Strategy: Drive the technical strategy and vision for agentic AI initiatives and major projects, ensuring alignment with business goals and industry best practices. Communicate complex agent architectures to non\-technical stakeholders, anticipate potential objections, and influence others to adopt a point of view.
- Leadership: Lead cross\-functional teams to design, develop, and deliver high\-impact agent\-powered projects on time and within budget. Coordinate activities and tasks of other team members, working independently and needing guidance only in the most complex situations.
- Architectural Excellence: Architect, design, and develop complex agentic systems and applications built on Google ADK and similar platforms, ensuring high standards of performance, scalability, and reliability. Design multi\-agent coordination patterns, task delegation workflows, and agent communication protocols. Collaborate with architects on mid\-level and high\-level design.
- Service Health and Quality: Ensure the health and quality of agent\-powered services, proactively identifying and addressing issues. Utilize service health indicators, agent performance metrics (task success rates, latency, cost), and telemetry for action. Conduct thorough root cause analysis for agent failures and implement measures to prevent future recurrences.
- Agent Operations: Oversee agent deployment, monitoring, and lifecycle management. Implement best practices for agent versioning, cost monitoring, performance optimization, and iterative improvement. Build observability into agent workflows including execution tracing, decision logging, and failure analysis.
- Engineering Excellence Practices: Advocate for and implement best quality practices for agentic systems, hold a high bar for engineering excellence, and guide the team in maintaining service quality through comprehensive testing (including agent evaluation, prompt testing, tool validation).
- Testing: Build testable agents and agentic systems, define tests including agent evaluation and tool validation, automate tests using appropriate frameworks. Implement agent\-specific testing strategies including prompt validation, multi\-step workflow testing, tool invocation testing, and adversarial scenario testing.
- Mentorship: Provide technical mentorship and guidance on agent development, fostering a culture of learning and continuous improvement. Mentor junior engineers on building production\-grade agents, designing tool integrations, and implementing agent safety practices.
- Innovation: Stay current with emerging agentic AI technologies and platforms (Google ADK, Anthropic Claude SDK, LangGraph, AutoGen, Crew AI), advocating for their adoption where appropriate to drive innovation and productivity enhancement within the team. Explore new agent capabilities, tool integrations, and orchestration patterns.
Qualifications:
- Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.
- 7\+ years of professional software development experience, with 2\+ years building agentic AI systems or LLM\-powered applications in production.
- Deep expertise in one or more programming languages such as Python, Java, C\#, JavaScript, or TypeScript.
- Hands\-on experience building production agents using platforms such as Google ADK, Anthropic Claude SDK, LangChain, LangGraph, or similar agentic frameworks.
- Experience designing and implementing multi\-agent systems, agent orchestration patterns, and tool\-use architectures.
- Strong understanding of prompt engineering, context engineering, and agent design patterns (ReAct, Chain\-of\-Thought, tool use, multi\-agent collaboration).
- Experience integrating agents with enterprise systems through APIs, tools, and protocols (REST APIs, MCP, function calling).
- Extensive experience with software architecture and design patterns, including the ability to design and implement scalable, reliable agentic systems in a DevOps model.
- Proven track record of leading and delivering large\-scale, complex software projects including agentic AI initiatives.
- Proficiency with cloud technologies like GCP (Vertex AI, Google ADK), Azure (Azure OpenAI), AWS (Bedrock), and version control systems like GitHub.
- Strong problem\-solving skills and attention to detail, with a commitment to delivering high\-quality agent\-powered solutions.
- Proficiency in building telemetry and observability for agentic systems (execution tracing, decision logging, cost monitoring, success rate tracking).
- Strong leadership, communication, and interpersonal skills, with the ability to influence and drive agentic AI technical decisions across the organization and translate agent capabilities to non\-technical stakeholders.
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 $129,500 to $214\.015\. 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 $129K-$214K range is below the median for AI Software Engineer roles in our dataset (median: $183K across 194 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At UKG, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $219,250 based on 424 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($171K) sits 22% below the category median. Disclosed range: $129K to $214K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
UKG AI Hiring
UKG has 2 open AI roles right now. They're hiring across AI Software Engineer. Based in Seattle, WA, US. Compensation range: $214K - $240K.
Location Context
AI roles in Seattle pay a median of $236,900 across 267 tracked positions. That's 9% above the national median.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. 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: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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