Sr. Staff Engineer Software (Prisma AIRS) - NetSec
Palo Alto Networks · Bengaluru · Posted 2026-07-09
Tech stack: AWS, Azure, Docker, GCP, JavaScript, Kubernetes, React, TypeScript, Vue
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About the role
The Team The Engineering team is at the core of our products and services. We are a team of innovators, problem-solvers, and builders who are passionate about creating cutting-edge cybersecurity solutions. We work collaboratively to tackle complex challenges, from cloud-native security to threat intelligence and endpoint protection. Our work is critical to protecting our customers' digital way of life. Join our team building a cutting-edge multi-tenanted GenAI Security Platform that helps organisations validate and secure their AI systems against adversarial attacks. Responsibilities: Design and implement high-performance REST APIs with enterprise-grade multi-tenant isolation and strict security boundaries. Work on distributed systems architecture handling high-throughput workloads with mission-critical uptime requirements. Build responsive dashboards and administrative interfaces for platform management, data visualisation, and system configuration. Integrate multiple LLM providers, implement semantic search capabilities, and build intelligent agent workflows. Architect complex, multi-step AI evaluation pipelines for asynchronous job execution and large-scale data processing. Design and implement database schemas with proper indexing, query optimisation, and data isolation strategies. Build and maintain scalable micro-services with async/await patterns and type-safe code. Develop data-intensive UIs with real-time updates, complex state management, and intuitive user experiences. Deploy and manage containerised applications on Kubernetes with comprehensive observability. Write thorough tests (frontend and backend) and maintain high code quality standards with automated tooling. Collaborate on architecture decisions and mentor team members on best practices. Qualifications: Required Qualifications Proven experience building and scaling multi-tenant SaaS platforms with strict data isolation. Strong knowledge of API design, RESTful principles, and OpenAPI specifications. Proficiency in modern JavaScript frameworks (React, Vue, or Svelte) with TypeScript. Experience building data-intensive dashboards with complex visualisations and real-time data. Strong CSS/styling skills and responsive design principles. Demonstrated experience working with production AI/ML systems at scale. Practical experience integrating LLM APIs and managing inference at scale. Understanding of LLM operational challenges: rate limiting, cost optimisation, latency management, fallback strategies. Familiarity with AI agent frameworks (LangChain, AutoGen, MCP, or similar). Knowledge of prompt engineering, semantic search, and vector databases. Hands-on experience with one of the major cloud platforms (GCP, AWS, or Azure). Strong Kubernetes and Docker skills with production deployment experience at scale. Experience with workflow orchestration engines (Airflow, Prefect, Temporal, Dagster, or similar). Proficiency with infrastructure-as-code, GitOps, and CI/CD pipelines. Understanding of distributed systems, networking, load balancing, and service mesh architectures. Preferred Qualifications Familiarity with AI safety, red teaming, or adversarial ML concepts. Prior experience in cybersecurity, AppSec, or security research. Knowledge of OWASP Top 10, API security best practices, RBAC, and IAM. Background in observability platforms (Grafana, Prometheus, DataDog, or similar). Experience with distributed task queues, message brokers, and resilience patterns.
Qualifications
- Required Qualifications Proven experience building and scaling multi-tenant SaaS platforms with strict data isolation.
- Strong knowledge of API design, RESTful principles, and OpenAPI specifications.
- Proficiency in modern JavaScript frameworks (React, Vue, or Svelte) with TypeScript.
- Experience building data-intensive dashboards with complex visualisations and real-time data.
- Strong CSS/styling skills and responsive design principles.
- Demonstrated experience working with production AI/ML systems at scale.
- Practical experience integrating LLM APIs and managing inference at scale.
- Understanding of LLM operational challenges: rate limiting, cost optimisation, latency management, fallback strategies.
- Familiarity with AI agent frameworks (LangChain, AutoGen, MCP, or similar).
- Knowledge of prompt engineering, semantic search, and vector databases.
- Hands-on experience with one of the major cloud platforms (GCP, AWS, or Azure).
- Strong Kubernetes and Docker skills with production deployment experience at scale.
- Experience with workflow orchestration engines (Airflow, Prefect, Temporal, Dagster, or similar).
- Proficiency with infrastructure-as-code, GitOps, and CI/CD pipelines.
- Understanding of distributed systems, networking, load balancing, and service mesh architectures.
- Preferred Qualifications Familiarity with AI safety, red teaming, or adversarial ML concepts.
- Prior experience in cybersecurity, AppSec, or security research.
- Knowledge of OWASP Top 10, API security best practices, RBAC, and IAM. Background in observability platforms (Grafana, Prometheus, DataDog, or similar).
- Experience with distributed task queues, message brokers, and resilience patterns.
Responsibilities
- Design and implement high-performance REST APIs with enterprise-grade multi-tenant isolation and strict security boundaries.
- Work on distributed systems architecture handling high-throughput workloads with mission-critical uptime requirements.
- Build responsive dashboards and administrative interfaces for platform management, data visualisation, and system configuration.
- Integrate multiple LLM providers, implement semantic search capabilities, and build intelligent agent workflows.
- Architect complex, multi-step AI evaluation pipelines for asynchronous job execution and large-scale data processing.
- Design and implement database schemas with proper indexing, query optimisation, and data isolation strategies.
- Build and maintain scalable micro-services with async/await patterns and type-safe code.
- Develop data-intensive UIs with real-time updates, complex state management, and intuitive user experiences.
- Deploy and manage containerised applications on Kubernetes with comprehensive observability.
- Write thorough tests (frontend and backend) and maintain high code quality standards with automated tooling.
- Collaborate on architecture decisions and mentor team members on best practices.