Senior Principal Engineer, Developer Productivity

• 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.

• 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.

• Design, prototype, and productionize AI agents and workflows for

requirements, coding, code review, testing, documentation, deployment, and operations.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

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 [https://benefits.paloaltonetworks.com/].

$235,600.00 - $323,400.00/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.

All your information will be kept confidential according to EEO guidelines.

Is role eligible for Immigration Sponsorship?: Yes

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.

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