Sr. Staff Applied AI Engineer, GTM

Zscaler•Santa Clara, CA

1d•$180,000 - $225,000

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 advisor to GTM leaders, running discovery, shaping AI strategy and roadmaps, and guiding Build/Partner/Buy decisions based on business outcomes such as pipeline generated and seller productivity. The engineer will improve technical implementations, standards, reusable components, and playbooks used across the Applied AI team. The position is based onsite in Santa Clara, California, or remotely in the United States, and reports to the Sr. Manager, Applied AI Engineering, in the IT/Data Strategy department. The role partners with GTM leaders, RevOps, product owners, Sales Enablement, GTM Operations, and frontline managers to identify needs, deliver and secure solutions, support adoption, and measure impact.

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.
Experience shipping solutions used by customer-facing teams with measurable impact on GTM metrics, 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 to tools and data through MCP, and agent-to-agent frameworks.
Experience working directly with GTM leaders on ambiguous problems, running discovery, translating unclear expectations into concrete plans, and communicating trade-offs, risk, and architecture to technical and non-technical audiences.
Experience securing AI systems and accessed data, including least-privilege access and defending against prompt injection, data leakage, and unsafe or non-compliant outputs.
Strong engineering skills, experience deploying and running AI at scale, proficiency in Python, comfort integrating across GTM systems of record, and 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 paired with A/B or holdout experiments measuring business lift in the field.

Responsibilities

Assist with GTM AI strategy from discovery through delivery by working with GTM leaders, RevOps, and product owners to map the revenue lifecycle, establish the AI roadmap, and advise on feasibility, cost, risk, and Build/Partner/Buy decisions.
Build and deploy secure, production-grade AI solutions for revenue teams, including use cases based on retrieval-augmented generation, structured-data querying, and agentic workflows.
Manage solutions as products tied to revenue by creating monitoring and evaluation frameworks, implementing security controls for customer and commercial data, and measuring ROI against GTM KPIs.
Drive adoption and organizational change by partnering with Sales Enablement, GTM Operations, and frontline managers to roll out solutions, train users, and build feedback loops based on field workflows.
Improve reusable building blocks, including retrieval pipelines, reference architectures, GTM data connectors, and engineering playbooks, and mentor other engineers.
Run discovery with GTM leaders, convert ambiguous expectations into concrete plans, and communicate trade-offs, risks, and architecture to technical and non-technical audiences.
Secure AI systems and the data they access, including least-privilege access and defenses against prompt injection, data leakage, and unsafe or non-compliant outputs.
Deploy and operate AI systems at scale, integrating GTM systems of record and releasing and monitoring solutions on a major cloud platform.