Interested in this AI Agent Developer role at Palo Alto Networks?
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Santa Clara, California, United States of America Product Engineering Ref ID: JR-023410
Our Mission
At Palo Alto Networks®, we’re united by a shared mission—to protect our digital way of life. We thrive at the intersection of innovation and impact, solving real-world problems with cutting-edge technology and bold thinking. Here, everyone has a voice, and every idea counts. If you’re ready to do the most meaningful work of your career alongside people who are just as passionate as you are, you’re in the right place.
Who We Are
In order to be the cybersecurity partner of choice, we must trailblaze the path and shape the future of our industry. This is something our employees work at each day and is defined by our values: Disruption, Collaboration, Execution, Integrity, and Inclusion. We weave AI into the fabric of everything we do and use it to augment the impact every individual can have. If you are passionate about solving real-world problems and ideating beside the best and the brightest, we invite you to join us!
We believe collaboration thrives in person. That’s why most of our teams work from the office full time, with flexibility when it’s needed. This model supports real-time problem-solving, stronger relationships, and the kind of precision that drives great outcomes. Job Summary
Running a branch network day to day is still too manual. When something breaks, IT teams jump between tools, read logs, and run commands one at a time to find the cause. Keeping devices patched and secure takes the same kind of effort.
We want to change that. Palo Alto Networks is hiring a Product Manager to build AI agents that handle day-to-day branch operations: finding and fixing network problems, checking security posture, and recommending software upgrades. This work is part of Strata Cloud Manager and spans our NGFW and SASE products.
This is a hands-on role. You will write the specs, design the user experience, talk to customers every week, and work closely with engineering to ship. You will also own the product strategy and help us sell it, both directly to enterprises and through partners.
What You'll Do
- Set the product strategy and roadmap for AI agents that run branch operations: troubleshooting, posture checks, configuration health, and software upgrade recommendations.
- Start from the customer's problem, not the existing workflow. Figure out which tasks admins should never have to do by hand again.
- Talk to network admins, security teams, MSPs, and support engineers to learn how branch problems are found and fixed today, and where it goes wrong.
- Write clear PRDs and user stories. Spell out what each agent does, what data it uses, when it acts on its own, and when it asks a person for approval.
- Design how people work with agents. Build mockups and prototypes yourself, and test them with customers before engineering starts.
- Make sure agents explain their findings and actions so admins can check the work and trust it.
- Define safety rules for agents that make changes on live networks, including approvals, limits, and rollback.
- Track whether it's working: accuracy, time to fix issues, tickets avoided, upgrade success, and adoption. Use the results to decide what to build next.
- Define packaging and pricing so these features grow revenue and adoption.
- Help sell to large enterprise customers: lead briefings, run design partner programs, and turn early users into references.
- Make the product work for partners and MSPs, including multi-tenant management, delegated access, and a business model they can resell.
- Build sales and partner enablement: demos, playbooks, competitive comparisons, and training.
- Work closely with engineering, AI/ML, design, support, sales, marketing, and channel teams, and with other product teams across NGFW, SASE, and SD-WAN.
Qualifications
What We're Looking For
- 7+ years of product management experience in networking, network security, or IT infrastructure.
- A track record of shipping management, monitoring, or troubleshooting products. SaaS experience preferred.
- Experience building products that use AI, machine learning, or large language models.
- Experience with AI agents is a strong plus.
- Strong networking basics: TCP/IP, routing, DNS, DHCP, VLANs. You can work through a network problem from the physical layer up to the application.
- Good understanding of branch security: firewalls, SASE, SD-WAN, and identity-based access.
- Experience building product strategy end to end: market sizing, positioning, pricing, and business cases.
- Experience taking products to market through direct enterprise sales and through partners such as resellers, MSPs, and MSSPs.
- You write your own specs and build your own prototypes.
- Clear writing and speaking. You can explain technical ideas to engineers, executives, and sales teams.
- You work well with others, take ownership, and are comfortable when the plan isn't fully defined yet.
- Bachelor's degree in Computer Science, Electrical Engineering, or a related field, or equivalent experience.
Nice to Have
- Experience with wired switching (Spanning Tree, PoE, 802.1X, network segmentation) or enterprise Wi-Fi (RF design, roaming, client onboarding, RADIUS).
- Past work as a network engineer, support engineer, sales engineer, or architect before moving into product.
- Experience with cloud-managed networking or AIOps products.
- Experience building multi-tenant or MSP-ready platforms.
- Familiarity with how AI agents are built: retrieval, tool calling, multi-agent setups, and evaluation.
- Certifications such as CCNP, CCIE, CWNA, or PCNSE.
- Master's degree or MBA.
Travel
Some travel for customer meetings and industry events.
Compensation Disclosure
The compensation offered for this position will depend on qualifications, experience, and work location. For candidates who receive an offer at the posted level, the starting base salary (for non-sales roles) or base salary + commission target (for sales/com-missioned roles) is expected to be the annual range listed below. The offered compensation may also include restricted stock units and a bonus.
$176,800.00 - $286,000.00/yr Our Commitment
We’re trailblazers that dream big, take risks, and challenge cybersecurity’s status quo. It’s simple: we can’t accomplish our mission without diverse teams innovating, together.
We are committed to providing reasonable accommodations for all qualified individuals with a disability. If you require assistance or accommodation due to a disability or special need, please contact us at [email protected].
Palo Alto Networks is an equal opportunity employer. We celebrate diversity in our workplace, and all qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or other legally protected characteristics.
All your information will be kept confidential according to EEO guidelines.
Is role eligible for Immigration Sponsorship?: Yes
Salary Context
This $176K-$286K range is above the 75th percentile for AI Agent Developer roles in our dataset (median: $175K across 28 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 9,079 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At Palo Alto Networks, 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 in Demand for This Role
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 $215,000 based on 203 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($231K) sits 8% above the category median. Disclosed range: $176K to $286K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Palo Alto Networks AI Hiring
Palo Alto Networks has 14 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer, AI Product Manager, AI Software Engineer. Positions span Santa Clara, CA, US, Tel Aviv-Yafo, Tel Aviv District, Israel, Charlotte, NC, US. Compensation range: $286K - $367K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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