Sr. Staff Software Development Engineer

Zscaler (NASDAQ: ZS) accelerates digital transformation so customers can be more agile, efficient, resilient, and secure. The Zscaler Zero Trust Exchange™️ platform protects thousands of customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location. Distributed across 160+ public exchanges globally and thousands of private exchanges at the edge, the SASE-based Zero Trust Exchange is the world’s largest in-line cloud security platform.

We believe the future of work is Human + AI and are building an AI-native enterprise where human potential is amplified by machine intelligence to solve the world’s hardest security challenges. Driven by deep customer obsession, we are committed to the mission, outcome, and to each other. We bring these commitments to life through three core behaviors: ownership and collaboration, trust through outcomes and impact, and a challenge culture with ongoing feedback. Ready to make an impact at the company pioneering security transformation in the AI era? Join us at Zscaler.

Role

We are looking for a Staff Applied AI Engineer to join our team. This is an On-site (San Jose, California) or Remote (US) role, reporting to the Manager, Applied AI Engineering in the IT/Data Strategy department. The Staff Applied AI Engineer is a senior, hands-on AI builder embedded with business teams to turn ambiguous, high-stakes ideas into secured, production-grade solutions. Beyond engineering, this person acts as a trusted transformation advisor — running discovery with business leaders, shaping roadmaps, guiding Build/Partner/Buy decisions, and building alongside stakeholders. They also improve and expand the technical implementations, standards, and engineering playbooks the rest of the Applied AI team relies on, keeping the team's deliverables aligned with current best practices.

What you’ll do (Role Expectations)

Partner with business leaders and product owners from discovery through delivery to map workflows, identify high-value AI opportunities, shape roadmaps, and advise on technical feasibility, cost, and risk

Design and ship secure, production-grade AI solutions across retrieval-augmented generation, structured data querying, unstructured data mining, and agentic workflows, integrating them with core systems of record

Bring a product-ownership mindset to deployments by establishing monitoring and evaluation frameworks, implementing least-privilege access, and managing ROI, cost attribution, and support plans

Set engineering standards by expanding reusable building blocks, reference architectures, skills, and engineering playbooks while mentoring team members

Who You Are (Success Profile)

You thrive in ambiguity and view dynamic, complex environments as opportunities to build meaningful solutions from the ground up.

You act like an owner with a strong bias for action, operating with integrity and navigating seamlessly between high-level strategy and hands-on execution.

You are a high-trust collaborator who embraces a feedback-rich culture, delivering candid insights with clarity and respect to build trust across the team.

You are driven by innovation, bringing a deep curiosity for complex technical challenges and a passion for building scalable, secure solutions.

You are a pragmatic builder obsessed with shipping high-quality work, balancing technical excellence with rapid delivery of real business value.

What We’re Looking for (Minimum Qualifications)

6+ years of professional software engineering experience, including at least 3 years working directly on AI and/or ML projects

Hands-on experience across modern AI architecture components, including retrieval methods, reusable skill development, agentic orchestration, Model Context Protocol (MCP) integration, and agent-to-agent (A2A) frameworks

Proven track record of direct collaboration with business stakeholders to run discovery, translate ambiguous needs into actionable plans, and communicate trade-offs, risk, cost, performance, and architecture

Demonstrated experience securing AI systems and underlying data, including least-privilege access controls and defending against prompt injection, data leakage, and non-compliant outputs

Strong engineering foundation proficiency in Python, with experience deploying AI at scale on major cloud platforms (AWS, GCP, Azure), integrating across APIs, relational/vector databases, and enterprise systems, and monitoring production drift and failure modes

What Will Make You Stand Out (Preferred Qualifications)

Familiarity with memory management and long-term context architectures for AI agents

Direct experience with graph-based knowledge systems