IT Software Engineering Senior Manager, Palo Alto Networks

• 12+ years of experience building enterprise software products, platforms, and distributed systems, with significant experience in modern application engineering
• 4+ years of engineering management experience leading high-performing software engineering teams
• Demonstrated experience leading full-stack engineering teams across frontend applications, backend services, APIs, distributed systems, application data stores, integrations, and production operations
• Strong software architecture background with the ability to evaluate technical tradeoffs and guide senior engineers across application, data, distributed systems, integration, and AI architecture
• Strong hands-on technical foundation in modern backend engineering using Java, Python, Go, or comparable languages
• Experience building modern web applications using React, Next.js, or comparable frontend frameworks
• Practical experience building production AI-powered applications using LLMs, retrieval/context architectures, agents, intelligent workflows, or related AI technologies
• Understanding of AI application patterns including tool calling, structured outputs, RAG, context management, agent orchestration, evaluation, guardrails, observability, and human-in-the-loop patterns
• Strong understanding of distributed systems, APIs, event-driven architectures, messaging and streaming, asynchronous processing, caching, transactional data stores, and analytical data platforms
• Experience with cloud-native architectures on GCP, AWS, or Azure, including containers, Kubernetes, managed databases, messaging systems, CI/CD, and observability
• Experience designing applications that combine transactional data, analytical data, real-time signals, and unstructured enterprise information
• Strong understanding of enterprise Sales processes including account management, opportunity management, pipeline execution, forecasting, quoting, and seller workflows
• Experience integrating applications with major enterprise platforms such as CRM, ERP, data platforms, collaboration tools, or other systems of record
• Strong engineering fundamentals around automated testing, code quality, security, observability, reliability, performance, and production operations
• Demonstrated ability to lead ambiguous, zero-to-one initiatives and turn business problems into scalable software products and platforms
• Excellent communication skills with the ability to work effectively with engineers, product leaders, business executives, and enterprise architecture teams
• Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience

Desirable

• We are seeking a strong engineering leader to build and lead the next generation of AI-powered applications for our Sales organization
• This role will lead teams building modern enterprise applications, AI-powered seller experiences, intelligent workflows, agentic systems, and shared platform capabilities across the Sales technology ecosystem. The scope spans full-stack product engineering, backend services, AI and context architectures, data platforms, event-driven systems, and integrations with enterprise systems
• This is an application engineering leadership role. The ideal candidate has a strong software engineering and architecture foundation, has built production-grade applications at scale, and understands how to incorporate AI into real enterprise workflows, not just prototypes or isolated AI features
• The leader will operate as a player/coach: setting technical direction, developing strong engineers, making architecture decisions, staying close to implementation, and driving complex initiatives from concept through production
• You will partner closely with Product, Sales Operations, Data, AI, Security, and other engineering teams to build products that improve seller productivity, account intelligence, pipeline execution, opportunity management, forecasting, and the overall seller experience
• Engineering Leadership: Build, lead, and develop a high-performing team of full-stack, backend, AI, and platform engineers. Establish strong engineering practices while remaining close enough to architecture and implementation to guide critical technical decisions
• Sales AI Applications: Lead the architecture and delivery of AI-powered Sales applications including seller command centers, account and opportunity intelligence, conversational experiences, recommendations, next-best actions, forecasting, workflow automation, and agentic use cases
• Full-Stack Product Engineering: Own modern application development across frontend experiences, APIs, backend services, application data stores, asynchronous processing, event-driven systems, and integrations with enterprise platforms
• AI & Agentic Engineering: Drive practical adoption of LLMs, retrieval and context architectures, agents, tool calling, model orchestration, evaluation frameworks, observability, and human-in-the-loop workflows. Take AI capabilities from experimentation into reliable, secure, production-grade systems
• Application & Platform Architecture: Define scalable architecture patterns across applications, services, AI systems, data, and integrations. Make pragmatic architectural and build-vs-buy decisions based on product value, scalability, maintainability, cost, and speed
• Context & Intelligence Platforms: Build systems that bring together transactional data, analytical data, real-time signals, unstructured information, and enterprise knowledge to provide the context required by AI-powered applications and agents
• Data & Integration Architecture: Design reliable, high-volume data flows across enterprise systems using APIs, events, streaming, CDC, asynchronous processing, and appropriate integration patterns. Ensure applications own the data and state required to operate independently and reliably
• Enterprise System Integration: Integrate applications with systems such as Salesforce, quoting and ERP platforms, collaboration tools, data platforms, and internal services without tightly coupling the application architecture to any individual SaaS platform
• Agentic Workflows & Automation: Design systems where agents can reason over enterprise context, invoke tools and services, execute multi-step workflows, maintain state, and safely perform actions across enterprise applications
• Engineering Execution: Own delivery from architecture and technical design through implementation, testing, deployment, observability, production operations, and continuous improvement. Establish clear accountability for product and engineering outcomes rather than managing purely through project plans or story-point capacity
• AI-First Engineering Practices: Build an engineering culture that uses modern AI-assisted development tools to improve development velocity, code quality, testing, documentation, debugging, and operational efficiency while maintaining strong engineering rigor
• Platform Reliability: Establish engineering standards for CI/CD, automated testing, security, observability, performance, resiliency, cost efficiency, and production operations across the team’s applications and services
• Cross-Functional Leadership: Partner closely with Product Management, Sales Operations, business leaders, Data, AI, Security, Enterprise Architecture, and other engineering organizations to turn ambiguous business problems into well-defined products and scalable technical solutions
• Strategic Execution: Lead complex zero-to-one initiatives spanning multiple teams and systems. Identify architectural and organizational friction and create reusable platforms, patterns, and mechanisms that allow teams to move faster without sacrificing engineering quality