Senior Product Manager, Agentic AI

Avispa Technology•South San Francisco, CA

1d•Hybrid

About The Position

A leading biotechnology company is seeking a Senior Product Manager, Agentic AI, to lead the strategy, development, and execution of autonomous and semi-autonomous AI agents within a regulated healthcare environment. This role will focus on transforming manual workflows into AI-driven orchestration systems that improve operational efficiency, user experience, and quality while ensuring compliance with clinical safety and regulatory standards.

Requirements

7+ years of product management experience.
2-5 years of experience focused on AI/ML systems, intelligent automation, or related AI-driven products.
Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical field.
Practical understanding of large language models (LLMs), agentic AI frameworks (e.g., LangChain, AutoGen), and Model Context Protocol (MCP).
Strong systems-thinking capability with the ability to translate ambiguous business challenges into technical requirements and execution plans.
Experience collaborating cross-functionally with engineering, product, and operational stakeholders.

Nice To Haves

Experience deploying AI solutions within regulated industries such as healthcare, life sciences, or financial services is preferred.
Experience building scalable AI platforms or infrastructure supporting multiple teams is preferred.
Familiarity with clinical safety standards, EHR integrations, FHIR APIs, and healthcare compliance requirements is preferred.

Responsibilities

Define and execute the product vision, roadmap, and long-term strategy for agentic AI capabilities, including multi-agent orchestration, intelligent automation, and personalized digital experiences.
Identify high-friction manual workflows and translate business problems into structured technical requirements, decomposing processes into intents, workflows, and data dependencies suitable for AI-agent execution.
Partner closely with engineering and technical teams to design agent architectures involving LLMs, retrieval-augmented generation (RAG), tool integrations, memory management, state handling, and agent orchestration frameworks.
Establish governance, compliance, and human-in-the-loop (HIL) standards to ensure AI systems operate safely, reliably, auditably, and in alignment with HIPAA, clinical safety requirements, and promotional review committee (PRC) standards.
Develop evaluation frameworks and performance metrics to measure AI effectiveness, including operational efficiency, engagement, and resolution accuracy, while supporting scalable AI platform adoption across cross-functional teams.