Sr. Staff Applied AI Engineer, GTM

Zscaler•Santa Clara, CA

14h

About The Position

Zscaler is seeking a senior, hands-on Applied AI Engineer embedded with its go-to-market organization. The role turns ambiguous, high-stakes ideas into secure, production-grade AI solutions that support revenue outcomes. It also serves as a transformation-advisory partner to GTM leaders, guiding discovery, strategy and roadmap development, and Build/Partner/Buy decisions measured against business results such as pipeline generation and seller productivity. The engineer will build and deploy AI solutions for revenue teams, run each solution as a product with monitoring, evaluation, security controls, and ROI measures, and partner with field teams to drive adoption and improvement. The role also develops reusable technical building blocks, standards, and engineering playbooks for the broader Applied AI team. The position is onsite in Santa Clara, California, or remote in the United States, and reports to the Sr. Manager, Applied AI Engineering in the IT/Data Strategy department.

Requirements

At least 5 years of professional software engineering experience, including at least 3 years working directly on AI and/or ML projects.
Direct experience driving both AI strategy and execution for GTM functions such as Sales, Marketing, or Partner Enablement, including shipping solutions used by customer-facing teams with measurable GTM impact and fluency in the revenue lifecycle.
Hands-on experience with most components of modern AI solutions, including retrieval methods, reusable skill development, agentic orchestration, connecting tools and data through the Model Context Protocol (MCP), and agent-to-agent (A2A) frameworks.
Experience working directly with GTM leaders on ambiguous problems, running discovery, converting unclear expectations into a concrete plan, and communicating trade-offs, risks, and architecture to technical and non-technical audiences.
Experience securing AI systems and accessed data, including least-privilege access and defenses against prompt injection, data leakage, and unsafe or non-compliant outputs.
Strong engineering skills and experience deploying and operating AI at scale.
Proficiency in Python and comfort integrating across GTM systems of record.
Experience releasing and monitoring AI systems on a major cloud platform.

Nice To Haves

Proven ability to use AI technologies and workflows to drive measurable operational efficiency.
GTM experience at a B2B enterprise SaaS or cybersecurity company with complex, multi-motion sales; prior experience in RevOps, Sales Engineering, or GTM Strategy is a strong plus.
Direct experience with graph-based knowledge systems and long-term agent memory, including account and relationship graphs, GraphRAG, or persistent context across an account’s history.
Experience building automated evaluation frameworks, such as LLM-as-judge or regression testing for AI quality, ideally alongside A/B or holdout experiments measuring business lift in the field.

Responsibilities

Partner with GTM leaders, RevOps, and product owners to map the revenue lifecycle, develop the GTM AI roadmap, and advise on feasibility, cost, risk, and Build/Partner/Buy decisions.
Design, build, and deploy secure, production-grade AI solutions for revenue teams using retrieval-augmented generation, structured-data querying, and agentic workflows.
Treat AI solutions as products tied to revenue by building monitoring and evaluation frameworks, implementing protections for customer and commercial data, and establishing ROI against GTM KPIs.
Partner with Sales Enablement, GTM Operations, and frontline managers to roll out solutions, train users, and establish feedback loops informed by field workflows.
Improve reusable building blocks such as retrieval pipelines, reference architectures, GTM data connectors, and engineering playbooks.
Mentor other engineers and improve the technical implementations and standards used by the Applied AI team.