• Lead the end-to-end development of an internal-facing GenAI capability • Conduct competitive scans of the emerging Databricks ecosystem and adjacent tooling • Help define schemas, APIs, and contracts • Participate in biweekly Data Platform demos and engineering standups • Close collaboration and weekly 1:1s with the Principal Product Manager of Data Platform.

• Own the Data Governance & Accessibility program • Design scalable data architecture • Improve pipeline reliability and mentor data engineers

• Architect, implement, and optimize data platforms and pipelines specifically designed to support LLMs, Retrieval-Augmented Generation (RAG), and sophisticated AI agentic systems at Exabyte scale • Drive the adoption and deployment of agentic workflows and agent harnessing techniques to create autonomous, data-driven security features • Design and implement highly scalable, fault-tolerant, and cost-effective data solutions, emphasizing rapid iteration and high-quality deployment • Write elegant, production-ready code with a focus on performance, maintainability, and testing rigor, ensuring the ability to ship fast without compromising quality • Provide technical leadership and deep expertise in data modeling, normalization, and semantic cataloging for AI/ML workloads • Establish best practices for MLOps/DataOps surrounding LLMs, including monitoring, observability, and zero-touch recovery mechanisms for AI services • Actively mentor engineers, conducting technical workshops, leading design reviews, and strengthening the team's knowledge in cutting-edge AI platform technologies • Collaborate across the organization with Data Scientists, Product Managers, and other engineering teams to transform research prototypes into robust, production-grade services • Own the end-to-end lifecycle of critical data services: development, testing, deployment, and monitoring

• Serve as the primary delivery partner for the Streaming Intelligence product line and DPT, converting roadmap priorities into actionable project plans with clear milestones, owners, and dependencies. • Co-own quarterly and annual planning with the Principal PM—from ideation through go-to-market—ensuring engineering capacity, vendor timelines, and business priorities are aligned. • Track and report on product line KPIs defined by the Principal PM (accuracy benchmarks, processing latency, content coverage, downstream adoption) and surface risks or deviations early. • Partner with the data team on agentic AI enablement—coordinating efforts to surface data models and semantic views to the broader business through AI-powered interfaces, including usage tracking and accuracy assurance. • Manage delivery cadence for platform infrastructure initiatives including data governance rollouts, real-time streaming capabilities, and pipeline migration efforts—tracking phased delivery across multiple months with clear metrics of success. • Lead end-to-end program management for AI pipeline initiatives, including the game film analysis benchmarking program that evaluates vision models (e.g., Gemini Flash, Gemini Pro) against human-tagged ground truth across 28+ annotated fields per play. • Manage the cadence of model evaluation cycles: coordinating game imports, prompt engineering iterations, accuracy scoring, and cost analysis across multiple AI models. • Drive vendor integration workstreams—tracking deliverables, SLAs, and roadmap alignment with external computer vision and AI partners alongside the Principal PM. • Build and maintain project documentation for hybrid human-AI workflows, including escalation paths for ambiguous plays, quality review processes, and fallback handling. • Facilitate cross-team visibility across product, engineering, data, design, and QA to ensure aligned execution mapping back to PlayOn’s unified product objectives: Engaged Communities, Resilient Services, and New Customer Markets. • Own dependency mapping, risk management, and blocker resolution for strategic AI initiatives spanning multiple engineering teams. • Develop and maintain project dashboards, status reports, and executive-level communications that increase transparency of engineering progress to the broader business. • Provide governance and optimization for Atlassian tools (Jira, Confluence) to ensure standardized workflows, consistent estimation practices, and clear traceability from strategy to execution. • Champion operational improvements to planning cycles, stakeholder coordination, cross-org meetings, and feedback loops—creating transparency through standardized tooling and repeatable frameworks. • Actively incorporate AI tools (e.g., Claude, automation platforms) into your own delivery workflows: automating status reports, synthesizing meeting notes, generating risk analyses, and accelerating documentation. • Contribute to PlayOn’s broader AI maturity journey by demonstrating AI-augmented delivery practices that can be adopted across the PMO and engineering organization.