Sr. AI Engineer, CX

Gametime United

3d•$150,000 - $220,000

About The Position

We’re building a new kind of engineering function at Gametime — one that sits within our CX teams, not beside them. As our first AI Engineer on the Ops team, you’ll be embedded directly within Fan Ops and Marketplace Ops as an AI Accelerator — working side-by-side with our customer experience and marketplace specialists to make AI the default mode for how the team operates. Not an occasional tool. The foundation of how every operator at Gametime executes. Today, our ops teams are already resourceful and fast. Your job is to multiply what they can do. You’ll design and build production-grade agentic workflows that automate the repetitive, the analytical, and the operationally complex — from fan issue triage to seller risk monitoring to marketplace optimization workflows. You’ll teach non-technical teammates how to work with AI tools and build their own automations. And you’ll help establish the playbook for embedding AI capability across the entire Operations org. This is a hands-on, move-fast, build-first role. Your measure of success is the number of workflows you’ve permanently transformed and the degree to which operators in your cohort reach for an AI tool before anything else. If you’re looking to advise and document, this isn’t it. If you want to build things that go into production the same day and see the impact directly in resolution times and fan satisfaction, read on.

Requirements

4+ years of technical operations or software engineering experience, with at least 1 year building production LLM or agentic systems
Experience building and deploying tools that non-engineers use in production — not just internal dev tooling
Ability to work closely with non-technical stakeholders, translate their domain knowledge into technical requirements, and communicate what you’ve built in plain language
Demonstrated experience coaching or enabling others — formally or informally — with evidence that people you’ve helped actually changed how they work
Comfort with ambiguity — you’ll often be working from “here’s the problem” not “here’s the spec”

Nice To Haves

Experience in customer operations, CX tooling, trust and safety, or marketplace operations contexts
Background in ticketing, e-commerce, or two-sided marketplace environments
Familiarity with support tooling platforms
Familiarity with RAG pipelines, vector databases, and evaluation frameworks —particularly for knowledge retrieval and automated response generation
Experience with data tools (SQL, dbt, Snowflake) — enough to pull your own context without needing a data analyst for everything
Prior experience in an embedded or cross-functional engineering role
Background in consulting, solutions engineering, or other client-facing technical roles where you had to earn trust before transforming how someone works

Responsibilities

Identify and document the highest-leverage workflow transformations across Fan Ops and Marketplace Ops by deeply understanding how your stakeholders work and what slows them down
Design and build agentic workflows that automate high-value ops tasks — fan issue triage and routing, AI-assisted response drafting, refund and dispute resolution, seller risk scoring, event cancellation handling, SLA monitoring and escalation, and more
Integrate with external and internal Gametime data systems to build tools that operate at the speed and scale our fan experience demands
Build tools non-technical teammates can actually use — think clean UIs, Slack-based interfaces, scheduled agents, and knowledge retrieval systems — not just scripts that only you can run
Coach and partner with each operator through a progressive journey: from awareness, to first win, to regular AI integration, to full workflow transformation, to self-sufficiency
Recognize patterns across your cohort and systematically scale what works — a tool built for one agent should become reusable for their peers across Fan Ops and Marketplace Ops
Teach and coach operations teammates on how to work with AI tools, build automations, and think in workflows
Own the full lifecycle of what you build — from design through production deployment, monitoring, and iteration to impact
Explore and evaluate new agentic frameworks, LLM capabilities, and AI tooling as the space evolves rapidly
Help define the model for AI-embedded engineering across Gametime’s broader Operations org