Staff AI Marketing Systems Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff AI Marketing Systems Engineer based in United States. This is a high-impact, hands-on engineering role focused on building AI-native systems that transform how Sales and Marketing teams work. You’ll design, build, and operate production-grade agent workflows that surface intelligence, automate content creation, and respond to real-time deal and campaign signals. The role spans LLMs, retrieval, tool calling, agent orchestration, data platforms, and enterprise integrations. You’ll work directly with sellers, marketers, and cross-functional technical teams to understand workflows and turn them into reliable AI-powered products. A major focus will be building systems that are secure, permission-aware, observable, evaluable, and genuinely useful in day-to-day work. You’ll have significant autonomy to shape architecture, product direction, technical standards, and the roadmap from prototype through production. This is an opportunity to help define how AI becomes a trusted operating layer for modern go-to-market organizations. Discover and map Sales and Marketing workflows by working directly with sellers, product marketing managers, and campaign teams to identify inefficiencies, requirements, and opportunities for AI-driven improvement. Define agent runtime and application architecture covering context, state, tools, permissions, retrieval, evaluation, human review, routing, and execution. Design trusted knowledge flows that combine account, persona, product, competitive, and campaign intelligence while managing freshness, conflicting sources, provenance, and source-of-truth decisions. Build routing systems and specialist agents supporting sales asset generation, campaign briefs, competitive intelligence, copy production, account planning, and active deal workflows. Integrate AI capabilities with enterprise Sales and Marketing platforms such as Salesforce, Seismic, Gong, Notion, Snowflake, and Slack, while maintaining appropriate access boundaries. Establish permission-aware retrieval, audit trails, provenance, prompt-injection defenses, retention controls, and clear rules for autonomous versus human-approved actions. Develop evaluation frameworks, regression testing, release checks, and quality guardrails to ensure AI outputs are accurate, relevant, reliable, on-brand, and useful. Continuously evaluate agent performance using seller feedback, usage data, output quality metrics, and representative evaluation sets, improving prompts, retrieval, tools, models, and workflows. Own systems throughout their lifecycle, from rapid prototype through reliable production deployment, monitoring, optimization, and eventual retirement. Track workflow outcomes including time-to-information, content creation effort, editing requirements, adoption, response time, cost per request, citation coverage, source freshness, and output accuracy. Partner closely with Sales leadership, Marketing Operations, Data, Security, IT, Engineering, and other stakeholders to translate business needs into scalable technical solutions. Requirements: 6+ years of experience designing, building, and operating production AI systems that use LLM APIs, tool calling, retrieval, structured outputs, and agent workflows. Demonstrated experience shipping AI-enabled software that real users depend on, with the ability to explain what was built, how it was evaluated, what failed, and how it was improved. Strong knowledge of agent-system architecture, including agent boundaries, context and state management, permission-aware tools, evaluation, observability, failure handling, and human escalation. Experience with modern AI development tools and frameworks such as Claude, Codex, Cursor, Dust, LangChain, LangGraph, or comparable technologies. Strong technical judgment and the ability to determine when a problem is best solved with deterministic code, wo