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About This Role
Job Description
UW Information Technology has an outstanding opportunity for AI Agent Builder to join their team.
About this Opportunity
Reporting to a Technology Manager in Infrastructure Service and AI Platforms, the AI Agent Builder will support the artificial intelligence (AI) initiatives at the university and its three campuses. This role is a pivotal role in shaping and implementing our AI strategy to transform UW into an AI\-powered University. The AI Agent Builder is a core technical role within the AI Platforms team, focused on the design, development, deployment, and ongoing enhancement of AI\-powered agents and intelligent automation solutions that serve the university. The AI Agent Builder Engineer role will work within Service Management and AI Platform team under the UW's IT infrastructure Umbrella that provides critical technology support to all three campuses, UW Medicine, and research operations around the world.
This is a full\-time hybrid position with the expectation of being in the Seattle, U\-District office a minimum of 3 days per week.
Key Responsibilities
\[25%] AI Agent Design \& Development
- Demonstrated experience designing, building, and iterating on AI agents to improve performance, functionality, and user outcomes.
- Architect and implement advanced RAG pipelines, including embedding strategies, vector search optimization, contextual window management, and hybrid retrieval techniques.
\[25%] Administrative \& Workflow Automation
- Identify and automate repetitive administrative processes across departments using AI\-powered workflows.
- Integrate AI agents with university enterprise systems (e.g., SIS, LMS, HRIS, ERP, ticketing systems) via APIs and connectors.
\[15%] Research Support
- Build AI tools that assist researchers with literature review, data analysis, grant writing support, and knowledge synthesis.
- Support advanced use cases involving long\-context reasoning, structured data augmentation, and research corpus grounding.
\[20%] Custom Tools for Faculty \& Staff
- Develop bespoke AI\-powered tools tailored to departmental needs (e.g., document drafting assistants, scheduling agents, data query tools).
- Implement agent orchestration frameworks such as CrewAI or equivalent enterprise\-grade platforms; experience with nebulaONE or similar orchestration environments is highly desirable.
\[15%] Platform \& Operations
- Monitor agent performance, usage analytics, and user feedback to continuously improve deployed solutions.
- Implement guardrails, safety mechanisms, and evaluation frameworks to ensure responsible AI behavior.
Required Qualifications
To be considered for this opportunity your application must demonstrate you meet both the minimum qualifications and additional qualifications listed below. Equivalent education and/or experience may substitute for minimum qualifications except when there are legal requirements, such as a license, certification, and/or registration.
Minimum Qualifications
- Bachelor's degree in Computer Science, Data Science, Software Engineering, or a related field or experience.
- 3 \+ years of professional software development experience demonstrating strong computer science fundamentals and API integration expertise, professional experience in software development.
- Demonstrated experience in AI/ML or LLM\-based applications.
- Demonstrated portfolio of deployed AI agent solutions or automation tools (GitHub repositories, case studies, or equivalent evidence of hands\-on work).
- Demonstrated experience building AI agents, chatbots, or conversational AI systems using modern LLM frameworks.
- Hands\-on experience with LLM APIs (e.g., OpenAI, Anthropic, Google, or open\-source models) and prompt engineering.
- Strong understanding of retrieval\-augmented generation (RAG), embeddings, vector search, and contextual grounding strategies.
- Familiarity with vector databases (e.g., Pinecone, Weaviate, ChromaDB, pgvector) and embedding\-based retrieval.
- Experience with REST APIs, cloud platforms (AWS, Azure, or GCP), and containerization (Docker).
- Strong problem\-solving skills and ability to work both independently and collaboratively in a cross\-functional teams.
Preferred Qualifications
- Experience working in higher education, research institutions, or the public sector.
- Demonstrated experience designing, building, and iterating on AI agents
- Experience with enterprise AI orchestration platforms such as nebulaONE or comparable environments.
- Experience with agent\-to\-agent (A2A) coordination models and advanced tool\-use frameworks.
- Experience with fine\-tuning, evaluation, or alignment of language models.
- Knowledge of data privacy regulations (FERPA, HIPAA) and responsible AI principles.
- Experience building no\-code/low\-code tools or platforms for non\-technical users.
- Contributions to open\-source AI/ML projects.
- Experience with REST API and MCP integrations is highly desirable; familiarity with A2A integrations is a plus but not required.
Working Conditions
- Work in an open office environment and contribute to collaborative teamwork focused on problem\-solving.
- Daily interactions with other team members, subject matter experts and stakeholders at all levels of the organization.
- While the general working hours are within Monday through Friday, 8 a.m.\-5 p.m. the AI Agent Builder Engineer will, on occasion, need to adjust hours to accommodate the business needs and deadlines.
- Attend and occasionally present at conferences.
Compensation, Benefits and Position Details
Pay Range Minimum:
$87,624\.00 annual
Pay Range Maximum:
$142,392\.00 annual
Other Compensation:
*
Benefits:
For information about benefits for this position, visit https://www.washington.edu/jobs/benefits\-for\-uw\-staff/
Shift:
First Shift (United States of America)
Temporary or Regular?
This is a regular position
FTE (Full\-Time Equivalent):
100\.00%
Union/Bargaining Unit:
Not Applicable
About the UW
Working at the University of Washington provides a unique opportunity to change lives – on our campuses, in our state and around the world.
UW employees bring their boundless energy, creative problem\-solving skills and dedication to building stronger minds and a healthier world. In return, they enjoy outstanding benefits, opportunities for professional growth and the chance to work in an environment known for its diversity, intellectual excitement, artistic pursuits and natural beauty.
Our Commitment
The University of Washington is committed to fostering an inclusive, respectful and welcoming community for all. As an equal opportunity employer, the University considers applicants for employment without regard to race, color, creed, religion, national origin, citizenship, sex, pregnancy, age, marital status, sexual orientation, gender identity or expression, genetic information, disability, or veteran status consistent with UW Executive Order No. 81 .
To request disability accommodation in the application process, contact the Disability Services Office at 206\-543\-6450 or [email protected] .
Applicants considered for this position will be required to disclose if they are the subject of any substantiated findings or current investigations related to sexual misconduct at their current employment and past employment. Disclosure is required under Washington state law .
Salary Context
This $87K-$142K range is in the lower quartile for AI Agent Developer roles in our dataset (median: $212K across 45 roles with salary data).
View full AI Agent Developer salary data →Role Details
About This Role
AI Agent Developers build autonomous systems that can reason, plan, and take actions. They design multi-step workflows, tool-use frameworks, and orchestration layers that let LLMs interact with external systems. This is the frontier of applied AI engineering.
Agent development is where the most interesting (and hardest) problems in applied AI live right now. Making an LLM answer a question is straightforward. Making it reliably execute a 15-step workflow that involves calling APIs, reading databases, making decisions, and recovering from errors is an unsolved problem. You're building systems that have to work despite the fact that the underlying model is non-deterministic.
Across the 3,824 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At University Of Washington, this role fits into their broader AI and engineering organization.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
What the Work Looks Like
A typical week includes: designing the action space and tool definitions for a new agent use case, debugging why the agent chose the wrong action sequence on a specific input, building evaluation frameworks that test agent reliability across hundreds of scenarios, optimizing the prompt chain for cost and latency, and implementing safety guardrails to prevent the agent from taking destructive actions. The work is equal parts engineering and empirical science.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
Skills Required
Deep experience with LLM APIs and agent frameworks (LangChain, CrewAI, AutoGen). Strong understanding of prompt engineering, function calling, and error handling for non-deterministic systems. Python is standard. Experience with orchestration patterns, state management, and workflow engines adds significant value.
The best agent developers think like systems engineers. They design for failure modes, build observability into every step, and understand that agent reliability is the product. Expertise in evaluation methodology for non-deterministic systems is the differentiator. Can you measure whether your agent works 'well enough'? Can you find the edge cases where it breaks?
Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
Compensation Benchmarks
AI Agent Developer roles pay a median of $252,000 based on 90 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($115K) sits 54% below the category median. Disclosed range: $87K to $142K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
University Of Washington AI Hiring
University Of Washington has 1 open AI role right now. They're hiring across AI Agent Developer. Based in Seattle, WA, US. Compensation range: $142K - $142K.
Location Context
AI roles in Seattle pay a median of $228,000 across 1,009 tracked positions. That's 14% above the national median.
Career Path
Common paths into AI Agent Developer roles include Software Engineer, LLM Engineer, Prompt Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, Head of AI Engineering.
Build agents. That's the portfolio. Take an open-source agent framework, build something that completes a non-trivial multi-step task, evaluate it rigorously, and document what you learned about reliability, cost, and failure modes. The field is new enough that practical experience counts for more than credentials.
What to Expect in Interviews
Interviews focus on systems thinking and reliability engineering. Expect questions about agent architecture: how you'd design a multi-step workflow with error recovery, how you'd evaluate agent performance, and how you'd prevent agents from taking destructive actions. Coding exercises often involve building a simple agent with tool use and evaluating its behavior across different scenarios. Discussion of safety and guardrails is increasingly common.
When evaluating opportunities: Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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