Sr. AI Engineer, CX

gametimeunited; United States; On-site; USD 130,000 - 180,000; Full time

Job summary

Gametime is hiring an AI Engineer embedded in Fan Ops and Marketplace Ops to scale AI across operations. You will build production-grade agentic workflows, automate fan issues, refunds, and risk monitoring, and partner with customer experience teams to deliver measurable improvements.

The role emphasizes hands-on delivery, tool-building for non-technical teammates, and rapid production deployment to influence resolution times and fan satisfaction.

Qualifications

• 4+ years in technical operations or software engineering with LLM/agentic focus
• Experience delivering production tools usable by non-engineers
• Ability to translate stakeholder needs into technical requirements
• Proven coaching or enabling others to change how they work
• Comfort working with ambiguity and undefined specs

Responsibilities

• Identify high-leverage workflow transformations for Fan Ops and Marketplace Ops
• Design and build agentic workflows automating high-value tasks
• Integrate with Gametime data systems to operate at speed and scale
• Create clean UIs, Slack interfaces, and knowledge retrieval tools
• Coach operators on AI tools and automation
• Own end-to-end lifecycle from design to production and monitoring
• Explore new AI tooling and model integration across Ops

Skills

Production LLM systems

Agentic workflows

Tools for non-engineers

Stakeholder communication

Coaching teammates

Ambiguity tolerance

Cross-functional collaboration

Job description

About Us:
Live experiences help people cross today's digital divide and focus on what truly connects us - the here, the now, this once-in-a-lifetime moment that's bringing us together. To fulfill Gametime's mission of uniting the world through shared experiences, we make it easy for people to discover and access the live experiences that matter most.

With platforms on iOS, Android, mobile web and desktop supporting more than 60,000 events across the US and Canada, we are reimagining the event ticket industry in order to move at the speed of life.

The Role

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.

What You'll Do

• 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 We’re Looking For

Required

• 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"

Preferred

• 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

What Success Looks Like

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