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About This Role
Company Description
It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy. That moment inspired Fred to build a company that could do that for everyone—freeing people from busywork so they could focus on meaningful work. Today, ServiceNow is the AI control tower for business reinvention. Our ServiceNow AI platform brings together any AI, any data, and any workflow— helping 85% of the Fortune 500® work smarter, faster, and better. We're building an AI\-native culture where technology and talent are unstoppable together. And we're just getting started.
Join us to put AI to work for people.
Job Description About the Role
You'll own the end\-to\-end technical relationship with enterprise customers, translating complex business challenges into high\-impact AI solutions built on the Moveworks Platform. This is a customer\-facing, field\-based role — you'll be the primary technical point of contact throughout the full implementation lifecycle, from solution design through launch. You are equally comfortable whiteboarding with a customer's IT team in the morning and collaborating with internal engineering and product teams in the afternoon.
This is not a back\-office engineering role — you will be in front of customers regularly and are expected to travel up to 25% of the time.
What You'll Own
- Customer Technical Relationships: Serve as the primary technical owner across 5–8 enterprise customer engagements simultaneously, driving adoption and measurable outcomes across the full Moveworks implementation lifecycle.
- Solution Design \& Architecture: Partner with customers to architect and deliver high\-impact AI solutions that solve real business challenges — leveraging the Moveworks Platform in innovative and meaningful ways.
- Integration \& Implementation: Design and build secure, performant integrations between the Moveworks Platform and customer enterprise systems including ServiceNow, Workday, Okta, Jira, and others.
- Strategic Advisory: Serve as a trusted technical advisor, helping customers develop their Agentic AI roadmap and upskilling their teams to operate the platform independently over time.
- Product Feedback Loop: Synthesize on\-the\-ground customer feedback and technical gaps, working directly with Moveworks Engineering and Product teams to influence platform evolution.
- Reusability \& Knowledge Sharing: Generalize successful customer solutions into reusable templates and share learnings broadly across the team.
About You
You are a technical generalist who genuinely enjoys working directly with customers. You thrive at the intersection of engineering, consulting, and customer success — and you're equally energized by solving a complex integration problem and presenting a solution narrative to a customer's executive team.
- Customer\-First Mindset: You have strong communication and relationship skills, and you're compelled to develop and deliver compelling solution narratives through high\-quality artifacts — architecture diagrams, solution proposals, SOWs — tailored to both technical and business audiences.
- Technical Mastery: You have a strong grasp of API\-based systems integration, LLM\-based systems design including prompt engineering, context engineering, and data modeling.
- Curiosity \& Adaptability: You are a rapid learner with high technical aptitude — equally curious about what's happening under the hood and why a business process works the way it does.
- Product Judgment: You have strong product taste and are obsessed with delivering exceptional experiences for end users.
- Entrepreneurial Grit: You lean into ambiguity, navigate legacy systems with patience, and actively seek exposure across engineering, product, and business functions.
- Accountability: You have a strong sense of personal accountability to both customers and internal teammates who rely on your expertise.
Qualifications Required:
- Experience in leveraging or critically thinking about how to integrate AI into work processes, decision\-making, or problem\-solving. This may include using AI\-powered tools, automating workflows, analyzing AI\-driven insights, or exploring AI’s potential impact on the function or industry.
- 5\+ years of experience in a customer\-facing technical role — Solutions Engineer, Customer Success Engineer, Solutions Architect, Implementation Consultant, or Consulting Engineer
- Demonstrated track record of driving successful technical adoption with mid\-to\-large enterprise customers
- Experience designing, building, and launching full\-stack workflows and automations leveraging REST APIs, iPaaS tools (Workato, Azure Functions, AWS Lambdas, ServiceNow Flow Designer), or scripting languages (Python, JavaScript, Golang)
- Willingness and ability to travel up to 25% of the time
- Ability to operate across multiple business functions and technical domains
Preferred:
- Familiarity with enterprise platforms such as ServiceNow, Jira Service Desk, Zendesk, Workday, or Okta
- Familiarity with Linux and Windows environments and command line
- Current on the latest AI tools, frameworks, and agentic design patterns — and thoughtful about how to apply them to build reliable, scalable solutions
For positions in this location, we offer a base pay of $143,200 \- $243,400, plus equity (when applicable), variable/incentive compensation and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure. Please note that the base pay shown is a guideline, and individual total compensation will vary based on factors such as qualifications, skill level, competencies, and work location. We also offer health plans, including flexible spending accounts, a 401(k) Plan with company match, ESPP, matching donations, a flexible time away plan and family leave programs. Compensation is based on the geographic location in which the role is located and is subject to change based on work location.
Compensation is based on the geographic location in which the role is located and is subject to change based on work location. Additional Information Work Personas
We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here. To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third\-party service.
Equal Opportunity Employer
ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, creed, religion, sex, sexual orientation, national origin or nationality, ancestry, age, disability, gender identity or expression, marital status, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements.
Accommodations
We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact [email protected] for assistance.
Export Control Regulations
For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities.
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Salary Context
This $143K-$243K range is above the median for AI Agent Developer roles in our dataset (median: $192K across 41 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 3,823 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At ServiceNow, 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 $245,040 based on 106 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($193K) sits 21% below the category median. Disclosed range: $143K to $243K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
ServiceNow AI Hiring
ServiceNow has 8 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer, AI Software Engineer. Positions span CA, US, San Francisco, CA, US, Mountain View, CA, US. Compensation range: $243K - $243K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,168 tracked positions. That's 26% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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