Build reusable agent skills, integrations, evaluation harnesses, templates, and reference implementations.
Work directly in codebases on high-value prototypes, critical components, integrations, and difficult engineering problems.
Evaluate coding assistants, agent platforms, models, and frameworks based on capability, security, reliability, cost, and enterprise fit.
Integrate approved AI capabilities into developer platforms, repositories, integrated development environments, CI/CD pipelines, and operational tooling.
Modernize Critical Systems
Apply AI-assisted analysis to uncover undocumented code behavior, dependencies, business rules, and operational failure modes.
Convert discovered knowledge into tests, technical documentation, dependency maps, and actionable modernization plans.
Lead targeted language, framework, architecture, and test-modernization initiatives while protecting business continuity.
Establish safe practices for AI-assisted refactoring, migration, test generation, and technical-debt reduction.
Partner with application owners and business stakeholders to validate that modernization preserves intended behavior.
Establish Safe and Reliable Agentic Workflows
Implement controls such as least-privilege access, sandboxing, approval checkpoints, time and cost limits, audit trails, and safe rollback.
Build evaluation frameworks that measure correctness, security, reliability, latency, cost, and workflow effectiveness.
Partner with Security, Privacy, Legal, Risk, and Compliance teams to translate policies into practical engineering controls.
Ensure AI-generated changes meet established standards for testing, review, security, and production readiness.
Design agent workflows that fail safely and remain observable, traceable, and understandable to human operators.
Drive Adoption and Developer Productivity
Work with engineering teams to identify and implement high-value AI use cases across a phased adoption model, from individual assistance to governed agentic workflows.
Develop reusable patterns and golden paths that teams can adopt without recreating infrastructure or controls.
Mentor engineers through architecture reviews, pairing, code reviews, workshops, and technical demonstrations.
Build a community of practitioners who can scale successful methods across the organization.
Communicate technical decisions, tradeoffs, progress, and risks to engineering leaders, executives, and business stakeholders.
Measure Engineering Outcomes
Establish baselines and success criteria before introducing new AI workflows.
Measure outcomes through lead time, review cycle time, test effectiveness, change-failure rate, security findings, recovery time, developer experience, and operating cost.
Run controlled pilots and use the results to determine whether capabilities should be expanded, redesigned, or retired.
Distinguish meaningful engineering improvement from activity measures such as tool usage, prompt counts, or generated code volume.
Contribute to External Technical Leadership
Represent the organization in selected conferences, technical forums, partner discussions, and industry communities.
Publish practical technical content, reference architectures, case studies, and lessons learned.
Bring relevant industry practices and emerging technologies back into the organization.
Build external credibility through demonstrated engineering outcomes and clear technical guidance.
Success in This Role
Production-ready AI engineering workflows deliver measurable improvements in delivery speed, quality, developer experience, or operational reliability.
Reusable patterns for agent orchestration, evaluation, security, and observability are adopted by multiple engineering teams.
Brownfield modernization and greenfield development both benefit from safe, repeatable AI-enabled workflows.
Engineering teams can adopt approved AI capabilities consistently without weakening security, compliance, or production standards.
Engineering and business leaders have a credible measurement framework for AI-enabled productivity, quality, and cost.
Senior engineers and architects view this role as a trusted source of hands-on technical guidance.
Qualifications
Required Qualifications
15 years of related experience with a Bachelor’s degree; or 12 years and a Master’s degree; or a PhD with 8 years experience in software engineering field, or equivalent demonstrated technical impact.
Experience operating at Senior Principal, Principal or equivalent scope across multiple teams or engineering domains.
Strong hands-on software-engineering skills and a recent record of building and operating production systems.
Practical experience with large language model APIs, tool-using agents, agent orchestration, context engineering, evaluation frameworks, and AI observability.
Experience integrating AI capabilities into developer workflows, engineering platforms, or enterprise applications.
Strong knowledge of distributed systems, APIs, event-driven architectures, CI/CD, cloud infrastructure, testing, observability, and production operations.
Experience modernizing complex, business-critical, or poorly documented systems.
Demonstrated ability to influence technical direction across organizations without direct authority.
Clear communication with engineering, executive, and non-technical audiences.
Preferred Qualifications
Experience introducing AI-assisted or agentic development workflows across a large or regulated enterprise.
Experience with enterprise platforms such as ERP, CRM, HRIS, CLM, finance, or customer-support systems.
Experience implementing AI security controls for data protection, permission boundaries, auditability, and prompt-injection defenses.
Familiarity with developer-productivity frameworks such as DORA and SPACE.
Technical publications, open-source contributions, conference presentations, or other evidence of external engineering leadership.
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.
$243,000.00 - $335,000.00/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 accommodations@paloaltonetworks.com.
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
