CXO AI Engineer

Role overview A new founding team is being assembled to build agentic AI systems that automate manual work, surface risks before they escalate, and give leaders self-serve visibility into customer experience operations. The function handles tens of thousands of monthly customer contacts across chat, phone, and email, supporting SMB through enterprise customers with internal and partner teams in four countries. This is a founding-engineer opportunity: there is existing tooling to build on, but no inherited playbook, and the engineer will shape architecture, workflows, and culture from day one.

Responsibilities - Automate repetitive operational workflows including reporting, QA transcript analysis, knowledge article maintenance, and SOP-driven case handling, then deploy standardized automations across collaboration and CRM platforms at scale. - Proactively flag stalled handoffs, aged cases, SLA breach risks, and duplicate cases; review bot transcripts to confirm agents behave as expected. - Deliver automated weekly and monthly business intelligence on cadence, covering site health, volume, SLA, and escalation or repeat-contact patterns, and give leaders self-serve access to the underlying data. - Build memory architecture that combines staffing, feedback, transcripts, and agent activity so bots retain context across interactions and anticipate what is coming next. - Connect orchestration layers and custom connectors to automate end-to-end customer experience workflows and productize internal tools. - Measure operational reliability and system accuracy against business metrics such as contacts deflected, cost avoided, and hours saved, and design escalation patterns so the system knows when to surface an issue to a human.

Requirements - 5+ years building and deploying production systems, with strong Python skills; SQL strongly preferred. - 2+ years working with LLMs in production, including prompt configuration, tool use, function calling, RAG pipelines, and agentic frameworks such as MCP or custom orchestration layers. - Hands-on experience building LLM orchestration and integration layers that connect AI agents to business systems, covering multi-step workflows, tool coordination, memory management, and error recovery. - Strong understanding of context engineering: structuring context, routing logic, and retrieval pipelines to ensure model accuracy at scale. - Ability to measure operational reliability of AI systems and confirm they deliver the intended business outcomes. - Comfort with ambiguity and the ability to define the approach rather than just execute a defined spec.

Nice to have - Partnering with data teams to build structured data layers that keep AI outputs trustworthy. - Productizing successful approaches so they scale across the organization.

Benefits and work setup - Remote-first model with physical workspaces in San Jose, CA and Draper, UT, plus opportunities for in-person collaboration when it counts. - Geographic salary zones: Zone 1 (San Francisco Bay Area, New York City, Seattle, Los Angeles County) $117,400–$146,800 USD; Zone 2 (other California, Austin TX, Massachusetts) $105,600–$132,000 USD; Zone 3 (Utah, Dallas TX, Houston TX, Florida, North Carolina, Illinois, Colorado, Arizona, Georgia, Oregon, Pennsylvania) $99,800–$124,700 USD. - 100% paid employee medical, dental, and vision coverage (HMO, PPO, or HDHP), HSA and FSA accounts, life and short- and long-term disability coverage, and an Employee Assistance Program. - 11+ observed holidays plus wellness days and flexible time off, an Employee Stock Purchase Program with discounts, and wellness, fitness, recognition, and referral programs.