AI Product Manager – Life Sciences & eClinical Platforms
Role Type: Full-Time
Company Description
Cloudbyz is a next-generation eClinical platform, built natively on Salesforce, designed to modernize and streamline clinical trial operations for life sciences organizations. The platform eliminates silos by integrating critical functions like CTMS, EDC, eTMF, RTSM, ePRO & eCOA, and more into a single, comprehensive solution.
Trusted by pharmaceutical companies, biotechs, medical device innovators, and CROs, Cloudbyz ensures real-time oversight, operational efficiency, and regulatory compliance throughout the trial lifecycle. Known for its secure, scalable, and easily configurable platform, Cloudbyz helps organizations achieve faster study start-up, centralized data management, and improved cross-functional collaboration.
The company is based in Warrenville, IL, and is committed to advancing smarter trials worldwide.
Role Summary
The AI Product Manager will own the strategy, roadmap, and delivery of BrüAI-powered products and AI Agents across Cloudbyz's unified eClinical platform. You possibles translate real clinical, regulatory, and operational problems into scalable AI solutions that drive productivity, compliance, and business outcomes.
This role sits at the intersection of clinical operations, AI engineering, and SaaS product leadership and will directly influence how AI transforms clinical trials, safety, regulatory, and financial workflows.
Key Responsibilities
- Define and own the AI product vision for AI Agents, automation, and intelligence across CTMS, EDC, eTMF, Safety, RTSM, and CTFM < rete>Identify high-ROI AI use cases from customer workflows and regulatory requirements
- Build and maintain the AI product roadmap aligned to business and customer priorities
- Prioritize features based on impact, feasibility, compliance, and adoption
- Work directly with clinical operations, QA, regulatory, and safety teams at customer organizations
- Translate workflows (e.g., study startup, TMF QC, safety case processing, regulatory reporting) into AI-ready user stories
- Define Human-in-the-Loop and compliance-first AI workflows Validate value through pilots, proofs-of-concept, and early adopters
AI & Engineering Collaboration
- Partner with AI engineers, data scientists, and platform teams to design models, pipelines, and architectures
- Define LLM, RAG, agent, and automation requirements
- Own data requirements, labeling strategy, evaluation metrics, and model performance KPIs
- Ensure AI features are scalable, explainable, and auditable
Compliance & Trust
- Ensure AI products meet GxP, 21 CFR Part 11, ICH-GCP, and Sky validation requirements
- Design audit trails, explainability, confidence scoring, and override workflows
- Define risk controls, bias monitoring, and safety guardrails