Senior Technical Product Manager, AI at 2K in Austin, Texas, United States

Job description

• We are seeking a Senior Technical Product Manager to build and ship the technology that 2K requires to safely, scalably, and efficiently leverage AI across the business.
• You will own the product pipeline for shared services and infrastructure for AI: identifying where 2K Technology should invest, defining what gets built, and driving it to a supported production service that studios and publishing teams depend on. This role suits a product manager who is highly focused on production outcomes and driving tangible business value. The work is judged on adoption, reliability, and cost. Success is enabling all 2K teams to maximize their leverage of 2K’s backend AI solutions.

Responsibilities

• Pipeline Ideas to Production: Turn concepts, prototypes, and business needs into shared-service products and infrastructure. Drive intake, prioritization, and acceptance, defining opportunity, customer, success metrics, and path to production before engineering commits.
• Find the Right Opportunities: Work with 2K teams to identify where a central build beats a local build or a licensed tool. Lead build, buy, and wrap calls with engineering, backed by evidence on cost, adoption, switching risk, and time to value.
• Partner and Communicate: Work with leaders across 2K to ensure AI investments extend existing capabilities. Publish a roadmap that customers and stakeholders can plan against, and report progress, cost, and risk to senior leadership.
• Measure Success: Track what tells you a product works: adoption, task success rate, inference cost per unit of work, latency, and quality regression.
• Build Governance In: Develop 2K's AI governance framework with Take-Two, Security, 2K Data, and 2K Trust & Safety. Instrument products so model usage, data provenance, and output handling are auditable by default, and teams building on central infrastructure inherit compliance rather than negotiate it.
• Engage Technically: Hold your own with engineers and data scientists on cost and latency tradeoffs, retrieval versus fine-tuning versus prompting, evaluation design, and the data pipelines underneath. Track a fast-moving field and tell 2K what actually matters.

Requirements

• A strong professional track record in product management, including direct experience taking AI or machine learning products from concept into production use by real customers.
• Demonstrated ownership of a production AI system, with accountability for availability, cost, and output quality over time.
• Practical fluency with modern AI application patterns, including retrieval, evaluation, fine-tuning, agentic workflows, and guardrails. You know the failure modes of each and when not to use them.
• Experience owning platform or infrastructure products with broad internal customer bases and competing stakeholder priorities.
• Comfort treating governance, risk, and compliance as product surfaces to design, not obstacles to route around.
• Ability to distill technical tradeoffs into clear decisions for engineering teams and non-technical executives alike.
• Comfort presenting to senior management and building organizational support for investments whose returns are measured in efficiency rather than direct revenue.
• Experience in the gaming industry, with an understanding of AAA development workflows, live service operations, or publishing.
• You actively build with AI tools yourself. Prototypes, evals, and working demos are part of how you communicate.
• Hands-on experience with AI gateways, model routing layers, or LLM ops tooling. Experience managing inference spend, including cost attribution, usage governance, and unit economics for AI workloads.
• Product experience in a regulated or audit-heavy environment where compliance requirements shaped the system architecture.

Skills

Required

• Product management
• AI/ML product development
• Production AI system ownership
• Governance, risk, and compliance
• Technical tradeoff communication
• Presenting to senior management

Preferred

• Gaming industry experience
• Hands-on AI tool building
• AI gateways, model routing, LLM ops
• Inference cost management
• Regulated/audit-heavy product experience