Sr. Staff Software Development Engineer

Zscaler•San Jose, CA

2d•$180,000 - $225,000•Hybrid

About The Position

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.

Requirements

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

Nice To Haves

Familiarity with memory management and long-term context architectures for AI agents
Direct experience with graph-based knowledge systems
Experience building automated evaluation frameworks such as LLM-as-judge methodologies or regression testing at-scale for AI quality

Responsibilities

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