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About This Role
Austin, Texas, United States Corporate Business Strategy Ref ID: JR\-017877
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!
This role is remote, but distance is no barrier to impact. Our hybrid teams collaborate across geographies to solve big problems, stay close to our customers, and grow together. You will be part of a culture that values trust, accountability, and shared success where your work truly matters.Job Summary
This is not a traditional business development or alliances role. You will be a strategic business developer who sits at the intersection of AI security, partner ecosystems, and business model innovation. Where most partnership roles focus on relationship management, this one demands something rarer: the ability to identify a market opportunity, size it rigorously, build the business case, negotiate the deal, and then stay in the room to make sure it actually lands with customers.
Three things set this role apart:
- Commercial Rigor: You bring genuine business modeling, pricing strategy, and opportunity sizing skills \- you can build a model, stress\-test it, and present it to a Business leader.
- Partner Ecosystem Depth: You understand how GSIs, management consulting firms, and technology ISVs think, sell, and build \- and you know how to structure agreements that create durable, mutual value.
Technical Credibility: You can translate a technology value proposition into a business value proposition, engage a CTO in a meaningful architecture conversation.
Palo Alto Networks is seeking an AIRS Strategic Partnership Business Development Manager or Senior Manager to drive the commercial expansion of the Prisma AIRS (AI Runtime Security) portfolio through high\-impact partnerships with Global System Integrators (GSIs), management consulting firms, and technology ISVs. This role sits within the AI Security Partnerships team and reports into the leadership of the AIRS strategic alliances function.
You will be responsible for the full lifecycle of AIRS partnerships \- from identifying and sizing opportunities, building use cases and business models, structuring and negotiating partnership agreements, to co\-developing partner offerings and taking them to market. You will work hands\-on: building slides, modeling financials, drafting agreements, and collaborating with partners to shape how Prisma AIRS is packaged, priced, and sold through the partner ecosystem.
The right candidate brings a consulting background \- rigorous, structured, and comfortable with ambiguity \- combined with genuine curiosity about AI and a strong instinct for where technology trends create commercial opportunity. You are as comfortable building a business value slide for a partner CEO as you are walking a GSI technical architect through a Prisma AIRS integration pattern.
KEY RESPONSIBILITIES
1\. Use Case Development \& Opportunity Identification
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The foundation of this role is the ability to identify where Prisma AIRS creates tangible value within partner\-led AI deployments \- and to articulate that value in commercial terms.
- Use Case Discovery: Identify, develop, and document compelling use cases for Prisma AIRS across GSI, consulting, and ISV partner contexts \- covering AI model security, agent security, runtime protection, and red teaming.
- Opportunity Sizing: Rigorously size partnership opportunities using market data, customer segmentation, and partner revenue analysis. Build credible models that withstand scrutiny from finance and executive leadership.
- Market Signal Translation: Stay ahead of developments in AI infrastructure, agentic systems, and enterprise AI adoption to identify where new use cases and partnership opportunities are emerging before they become obvious.
- Experimentation Mindset: Actively experiment with new use case hypotheses \- engaging partners in structured pilots and POCs \- to validate commercial potential before committing to full partnership structures.
2\. Business Modeling, Pricing \& Business Case Development
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You will own the financial and commercial architecture of AIRS partnerships, ensuring every deal is grounded in a rigorous, credible business case.
- Business Model Design: Develop and evaluate partnership business models \- including resell, co\-sell, embedded OEM, and managed service structures \- that align partner incentives with Prisma AIRS growth objectives.
- Pricing \& Commercial Structuring: Define pricing frameworks, discount structures, and revenue\-sharing models for partner\-led AIRS offerings. Ensure commercial terms are competitive, scalable, and operationally viable.
- Business Case Development: Build compelling, data\-driven business cases for partnership investments \- covering market opportunity, revenue potential, cost to serve, and strategic rationale \- tailored for both internal approval and partner alignment.
- Financial Modeling: Construct detailed financial models that project partnership revenue, partner economics, and return on investment across different growth scenarios. Comfortable owning a model end\-to\-end.
3\. Partnership Negotiation \& Agreement Closure
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You will lead the negotiation and closure of AIRS partnership agreements, navigating complex commercial, legal, and technical trade\-offs to reach durable, mutually beneficial outcomes.
- Deal Leadership: Drive partnership engagements from initial outreach through signed agreement \- owning the commercial narrative, managing partner stakeholders, and keeping momentum through complexity.
- Negotiation: Lead commercial negotiations with senior partner stakeholders including Heads of Alliances, Chiefs of Strategy, and General Counsel. Skilled at finding creative paths to agreement without sacrificing strategic value.
- Cross\-Functional Coordination: Work closely with marketing, finance, legal, and technology teams at Palo Alto Networks to align on deal structure, approval, and execution. Able to move quickly across functions without losing the thread.
- Stakeholder Management: Build and sustain senior relationships across partner organizations — engaging marketing, finance, legal, and technology leaders as peers. Comfortable in the room with executives and able to earn trust through substance.
4\. Partner Offering Development \& Go\-to\-Market
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Closing a deal is the beginning, not the end. You will work with partners to shape how Prisma AIRS is packaged and brought to their customers.
- Partner Offering Design: Co\-develop partner\-specific Prisma AIRS offerings \- defining the service scope, packaging, pricing, and value proposition \- tailored to how each partner type (GSI, consulting firm, ISV) goes to market.
- Joint GTM Planning: Build and execute joint go\-to\-market plans with partners, including co\-marketing commitments, sales enablement, pipeline targets, and customer success milestones.
- Commercial Enablement: Develop partner\-facing commercial materials \- business value slides, ROI frameworks, pricing summaries, and solution briefs \- that equip partner sales and consulting teams to position Prisma AIRS effectively.
- Customer Engagement: Support partners in customer\-facing settings \- from executive briefings to commercial conversations \- helping to close lighthouse deals that serve as the proof points for broader partner adoption.
5\. Communication, Slide Craft \& Executive Influence
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In this role, your ability to communicate with clarity and conviction \- in writing, in slides, and in person \- is a core competency, not a soft skill.
- Business Value Storytelling: Build slides and written materials that make complex technology and commercial propositions immediately accessible to business audiences. Comfortable distilling a nuanced argument to a single clear page.
- Dual\-Audience Communication: Able to move fluidly between technical and business audiences \- presenting an architecture diagram to a partner CTO and a revenue model to a partner CFO in the same day, adjusting register and depth accordingly.
- Executive Presence: Communicate with senior stakeholders \- internally across marketing, finance, legal, and technology, and externally at partner organizations \- with the gravitas and clarity expected at the VP and C\-suite level.
- AI\-Augmented Productivity: Comfortable using AI tools to accelerate research, drafting, and analysis \- and critically, able to identify and correct AI\-generated errors, hallucinations, and low\-quality outputs before they reach partners or leadership.
CRITICAL COMPETENCIES
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Business \& Commercial Acumen
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- Opportunity sizing and market analysis
- Pricing strategy and commercial model design
- Financial modeling and business case development
- Partnership agreement structuring and negotiation
- Understanding of GSI, consulting, and ISV business models and economics
Technical Fluency
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- Ability to translate technology value propositions into business value propositions
- Understanding of AI infrastructure, inference, and agentic architectures sufficient to engage partner technical teams
- Familiarity with Prisma AIRS, NGFW, and CDSS — or demonstrated ability to acquire product depth rapidly
- Comfortable using and critically evaluating AI tools in day\-to\-day work
Communication \& Slide Craft
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- Exceptional written and verbal communication skills
- Strong command of Google Slides and PowerPoint — able to build slides that are clean, structured, and persuasive
- Ability to produce executive\-quality business value narratives without extensive editing support
- Hands\-on: willing to draft, build, and iterate directly rather than delegating to support functions
Partner Ecosystem Knowledge
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- Understanding of how GSIs (Deloitte, Cognizant, NTT, Wipro) build practices and sell to enterprise customers
- Familiarity with management consulting firm structures and how they develop and monetize AI offerings
- Understanding of ISV go\-to\-market models including OEM, marketplace, and embedded integration patterns
PARTNER ECOSYSTEM SCOPE
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This role will build and manage commercial partnerships across three partner categories:
Global System Integrators (GSIs)
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Deloitte, Cognizant, NTT, Wipro, Infosys, and similar firms. Focus on embedding Prisma AIRS into GSI\-led AI delivery practices, managed security services, and enterprise transformation programs. Deals typically involve practice development agreements, co\-sell arrangements, and managed service commercials.
Management Consulting Firms
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McKinsey, BCG, Accenture, and similar firms. Focus on co\-developing AI security frameworks and positioning Prisma AIRS within client advisory and transformation programs. Commercial structures emphasize joint solution development, thought leadership, and referral or influence arrangements.
Technology ISVs
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Enterprise AI platform providers, AI infrastructure companies, and complementary security ISVs. Focus on productized integrations, OEM arrangements, and marketplace listings that embed Prisma AIRS natively within partner platforms and distribution channels.
CRITICAL KNOWLEDGE AREAS
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Prisma AIRS \& Palo Alto Networks Platform – Essential
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- Prisma AIRS Portfolio: Technical mastery of AI Model Security, AI Red Teaming, AI Runtime Security, and AI Agent Security—including their core capabilities, deployment architectures, and API\-driven interfaces.
- Next\-Generation Firewalls (NGFW): Command of architecture and deployment patterns across data center, cloud, and hybrid environments; specifically, how firewall policy enforcement secures enterprise AI workloads.
- CDSS (Cloud\-Delivered Security Services): Familiarity with Threat Prevention, DNS Security, and Advanced WildFire, with an emphasis on their application to protecting modern AI infrastructure and mission\-critical workloads.
- AI Gateway Configuration: Experience with inline security enforcement for LLM traffic, including prompt inspection, response filtering, and seamless integration with AIRS runtime control mechanisms.
Partner Ecosystem \& Security Fundamentals – Essential
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- GSI Technology Stacks: Familiarity with how Deloitte, Cognizant, NTT, and Wipro architect and deliver AI solutions for enterprise clients; specifically, how they monetize and scale transformation practices.
- Identity \& Access Management (IAM): Knowledge of securing agent identities, service accounts, and API key lifecycles to maintain least\-privilege access within complex, agentic AI ecosystems.
Cloud\-Native AI Services: Command of AWS, Azure, and GCP AI/ML portfolios and their respective integration patterns with enterprise\-grade security controls and enforcement mechanisms.
Qualifications
REQUIRED
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- Experience: 8\+ years of experience in strategic business development, partnerships, management consulting, or a combination \- with a track record of closing complex, multi\-stakeholder commercial agreements.
- Business Modeling \& Financial Analysis: Demonstrated ability to size markets, build financial models, develop pricing frameworks, and construct executive\-quality business cases. Comfortable owning quantitative analysis end\-to\-end.
- Negotiation \& Deal Closure: Proven track record of negotiating and closing partnership agreements with senior stakeholders, navigating commercial, legal, and technical trade\-offs to reach signed outcomes.
- Consulting Background: Experience in management consulting or a consulting\-adjacent role — bringing structured problem\-solving, rigorous communication standards, and the ability to operate with ambiguity.
- Technical Literacy: Sufficient technical understanding to translate AI and cybersecurity technology value propositions into business terms, engage meaningfully with partner technical teams, and evaluate integration feasibility.
- Slide \& Communication Craft: Strong ability to build clean, structured, persuasive slides and written materials for executive audiences. Hands\-on \- you build the materials yourself.
- Cross\-Functional Influence: Experience working across marketing, finance, legal, and technology functions to align on and execute complex partnership deals. Able to move quickly without losing stakeholder alignment.
- AI Proficiency: Actively uses AI tools to improve productivity and output quality. Critically, understands the limitations of AI\-generated content and knows how to identify, correct, and prevent low\-quality outputs.
- Partner Ecosystem Knowledge: Understanding of how GSIs, management consulting firms, and technology ISVs operate, sell, and build \- and how to structure partnerships that create value within their business models.
PREFERRED
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- GSI \& Consulting Sector Depth: Direct experience working at or extensively with major GSIs or top\-tier management consulting firms, with a clear understanding of their practice economics, delivery models, and partner procurement processes.
- Cybersecurity or AI Domain Knowledge: Prior experience in cybersecurity, AI infrastructure, or enterprise software partnerships. Familiarity with Prisma AIRS, NGFW, or CDSS is a meaningful differentiator.
- ISV \& Marketplace Experience: Experience structuring ISV partnerships, OEM agreements, or cloud marketplace listings as a vehicle for commercial scale.
- Product Management Exposure: Prior experience in or alongside product management, enabling strong alignment with product strategy and roadmap conversations.
Education: Bachelor's degree or higher in Business, Economics, Computer Science, Engineering, or a related field. MBA or equivalent advanced degree is a plus.
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. A description of our employee benefits may be found here.
$171,800\.00 \- $277,925\.00/yrOur 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? No. Please note that we will not sponsor applicants for work visas for this position.
Salary Context
This $171K-$277K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Palo Alto Networks, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($224K) sits 22% above the category median. Disclosed range: $171K to $277K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Palo Alto Networks AI Hiring
Palo Alto Networks has 25 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager. Positions span Santa Clara, CA, US, Austin, TX, US. Compensation range: $204K - $344K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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