Senior Product Manager, GenAI Platform Products

Role Summary

Senior Product Manager to lead the strategy and delivery of GenAI platform products—the core platform that enables development and deployment of GenAI-powered applications, agents, and MCP services. This platform provides unified access to LLM, embeddings, vector search prompt orchestration, model routing, and agent frameworks, enabling R&D teams to rapidly prototype, operationalize, and scale GenAI solutions and ultimately deliver new medicines for our patients.

Responsibilities

• Own and drive the vision, roadmap, development, and adoption of GenAI platform capabilities, ensuring a unified, governed, and high-quality experience for LLMs, embeddings, vector search, prompt orchestration, model routing, agent frameworks, and MCP services.
• Define the strategic direction for GenAI capabilities, enabling scalable, compliant, production-ready GenAI and agentic applications across R&D.
• Conduct ongoing customer discovery with scientists and AI/ML practitioners to identify emerging needs and translate them into actionable product requirements.
• Lead technical product discussions with engineering and scientific leaders to clarify objectives and shape platform direction.
• Collaborate with stakeholders to define platform features, requirements, and success criteria aligned with scientific use cases and business goals.
• Drive agile product execution with engineering and program teams, owning prioritization, backlog management, and delivery of high-quality platform releases.
• Ensure seamless integration with the Data Platform and AI/ML Platform to enable shared data standards, consistent data and model lifecycle management, and full interoperability across GenAI-powered applications.
• Coordinate and align roadmap with R&D platforms to ensure interoperability, governance alignment, and a unified enterprise data, compute, AI, and application ecosystem.
• Lead platform launches and change-management activities to ensure clear communication, training, and successful adoption across R&D.
• Monitor platform usage and performance, analyze feedback and telemetry, and drive continuous improvements to enhance usability, reliability, and scientific impact.

Qualifications

• Required: PhD + 2 years, or Masters + 4 years, or Bachelors + 6 years total experience.
• Required: 4+ years of experience in product management with a proven track record of shipping 0-to-1 platform capabilities powered by GenAI, LLMs, or autonomous agents in a commercial or large-scale enterprise setting.
• Required: Experience defining platform strategy for modern GenAI systems, including hands-on familiarity with RAG pipelines, embedding services, prompt templates, agent frameworks, vector databases, and evaluation tooling.
• Required: Experience with cloud-native architectures (e.g., AWS, Azure, GCP), API design, high-performance serving infrastructure, and platform components required to securely deploy and scale LLM-based applications for enterprise use.
• Required: Experience working closely with platform engineering, MLOps, and security teams to build reliable, governed, reusable GenAI capabilities that accelerate development for multiple downstream product teams.
• Required: Experience driving platform adoption, governance, and developer enablement, including SDKs, templates, guardrails, and onboarding materials for cross-functional teams.

• Preferred: Direct product management experience designing and launching GenAI agents and platform capabilities that leverage tool use (APIs, function calling), planning modules, and multi-step reasoning to support a broad set of enterprise or scientific workflows.
• Preferred: Hands-on software engineering or data science experience within a GenAI or ML platform team prior to transitioning into product management, with exposure to LLM infrastructure, RAG pipelines, and developer tooling.
• Preferred: Deep familiarity with modern transformer-based model architectures, with the ability to make platform-level strategic decisions between proprietary models, open-source models, domain-adapted models, and fine-tuning approaches.
• Preferred: Experience delivering platform capabilities that manage, index, or interpret complex, unstructured biomedical or scientific data through embeddings, vector stores, or structured retrieval frameworks.
• Preferred: Extensive knowledge of bioinformatics, computational biology, or cheminformatics, and a strong vision for how enterprise-scale GenAI platforms can power the next generation of scientific automation and agentic workflows.
• Preferred: Extensive platform product experience designing, optimizing, and implementing Model Context Protocols (MCP) or similar orchestration frameworks for LLM-powered agents, including context management, memory systems, prompt optimization, safety, and maintaining coherence over long-running tasks.
• Preferred: Hands-on experience with product management and technical collaboration tools such as Confluence, Jira, Miro, Monday, Notion, and Git-based documentation.
• Preferred: Previous experience in life sciences or biopharma R&D is a strong plus.