Senior Product Manager (R&D – AI Tools)

PLAY, GROW and WIN

To be a part of Virtuos means to be a creator.  

At Virtuos, we harness the latest technologies to make games better and more immersive than ever before. That is why we pride ourselves in constantly pushing the boundaries of possibility since our founding in 2004. 

Virtuosi is a team of experts – people who have come together to share their mutual passion for making and playing games. People with the same enthusiasm for exploring new ideas and the constant drive to excel in their field. People who believe in earning success through dedication.

At Virtuos, we are at the forefront of gaming, creating exciting new experiences daily. Join us to Play, Grow and Win – together.

ABOUT THE POSITION

We are seeking an experienced Senior Product Manager to lead the vision, strategy, and execution of data analytics platforms, telemetry, backend ML services, and AI services governance across R&D.

This role sits at the intersection of data, platform engineering and applied AI. You will define how data and telemetry power insights, how ML capabilities are delivered at scale through an internal AI services gateway platform, and how AI services are governed through transparent usage tracking and chargeback models.

You will collaborate closely with R&D engineering, and product owners across game engineering, art, production, and G&A teams to drive data-driven decision-making, scalable ML adoption, and cost accountability across all AI initiatives.

• Define and own the product vision, roadmap, and strategy for telemetry and observability services: data analytics and reporting platforms, model lifecycle and serving capabilities) as well as AI usage tracking.
• Align product direction with R&D strategic goals, including scalability, adoption, cost efficiency, and governance.
• Own the end-to-end internal AI services gateway platform, including model onboarding, deployment, and serving capabilities, standardized APIs and services for ML/AI consumption and integration with AI tools, agents, and pipelines.
• Define platform capabilities to support model experimentation and iteration, production-grade inference services, monitoring, versioning, and lifecycle management.
• Drive reuse and standardization across teams to avoid fragmented ML implementations.
• Partner with ML engineers and platform teams to ensure reliable, scalable, and secure ML services.
• Design and evolve end-to-end telemetry architecture, ensure telemetry supports: ML model usage tracking, AI tool adoption and performance monitoring and cost attribution.
• Establish consistent telemetry standards across all AI and ML services.
• Define key metrics, dashboards, and reporting frameworks to measure: AI and ML service adoption and usage, model performance and impact, cost vs. value (ROI).
• Translate data into actionable insights for product and leadership decisions.
• Work closely with R&D engineers (platform, backend, data, ML), IT and Product owners across business domains translate business needs into clear product requirements and technical specifications.
• Own the end-to-end product lifecycle: discovery, prioritization, development, rollout, and iteration.
• Maintain and prioritize backlog based on impact, feasibility, and strategic alignment.
• Ensure platforms are production-grade, reliable, and scalable.
• Establish best practices for:  ML lifecycle governance (models, datasets, deployments), telemetry and instrumentation standards and AI/ML usage reporting.

• 5+ years of Product Management experience, preferably in data platforms, ML platforms, or infrastructure products.

• Strong understanding of: Data systems (pipelines, warehousing, telemetry), Machine learning lifecycle and model serving concepts.

• Experience working with engineering-heavy teams, including ML and platform engineering.

• Proven ability to deliver platform products at scale. 

• Strong analytical mindset with data-driven decision-making ability.

• Excellent communication and stakeholder alignment skills.

  
  
  

  Preferred Qualifications

  • Experience building or managing ML platforms.

  • Familiarity with AI/ML usage tracking and telemetry systems.

  • Experience with internal developer platforms or tooling ecosystems.

  • Experience in R&D or innovation-driven environments.

  • Understanding of data governance, AI governance, and responsible deployment.

  • Experience with FinOps, cost attribution, or chargeback models is a plus.