Senior AI Engineer (Customer Experience)

• 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
• 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
• What Success Looks Like:
• In 90 days, you’ve shipped production agents that Fan Ops and Marketplace Ops teams use weekly without your involvement
• You’ve materially reduced manual resolution time, improved fan satisfaction metrics, and freed up operator capacity
• At least multiple teammates can build and modify automations themselves — and they start new tasks by reaching for an AI tool first

Benefits

• Health Benefits: Medical, dental, and vision coverage for you and your dependents
• Equity: An ownership stake in the company you’ll be helping to build
• Flexible PTO: ‘Take what you need’ vacation policy (we actually mean it)
• Remote First: Fully remote company
• Remote Work stipend: Monthly stipend to offset the cost of internet and any home office updates
• Paid Parental Leave: Peace of mind for parents to dedicate their time to the care of their new child
• Gametime Credit: Monthly Gametime credit to experience the product for yourself
• Team Celebrations: Company meetups and events in different cities across the US- Ability to work closely with non-technical stakeholders, translate their domain knowledge into technical requirements, and communicate what you’ve built in plain language
• Comfort with ambiguity — you’ll often be working from “here’s the problem” not “here’s the spec”
• 4+ years of technical operations or software engineering experience, with at least 1 year building production LLM or agentic systems
• Demonstrated experience coaching or enabling others — formally or informally — with evidence that people you’ve helped actually changed how they work
• Experience building and deploying tools that non-engineers use in production — not just internal dev tooling
• Experience in customer operations, CX tooling, trust and safety, or marketplace operations contexts
• Familiarity with RAG pipelines, vector databases, and evaluation frameworks — particularly for knowledge retrieval and automated response generation
• Familiarity with support tooling platforms
• 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
• Experience with data tools (SQL, dbt, Snowflake) — enough to pull your own context without needing a data analyst for everything
• Background in ticketing, e-commerce, or two-sided marketplace environments