Aledade - Senior Technical Product Manager
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Requirements
• 8+ years of product management experience in technology, technology-enabled services, or SaaS. • Strong understanding of artificial intelligence and/or machine learning technologies. • Experience using data and primary research to inform solution design and build business understanding. • Familiarity with Agile teams, Jira, and the software development lifecycle. • Knowledge of APIs, DevOps, enterprise software development, and cloud infrastructure (e.g., Databricks, AWS, GCP, Azure). • Understanding of data engineering principles. • Ability to communicate technical trade-offs to non-technical stakeholders. • Skilled at translating ambiguous product requirements into a technical roadmap. • Preferred Knowledge, Skills and/or Abilities: • Experience leading AI/ML platform products and scaling platforms vertically and horizontally. • Track record of building and launching AI/ML platforms, tools, or internal services. • Deep knowledge of AI/ML product lifecycle, MLOps, and scalable AI infrastructure (training, deployment, monitoring, observability, reliability). • Experience with large language models (LLMs) and generative AI technologies, including prompt engineering strategies and VLMs. • Background as a data scientist or backend engineer. • Fluency in translating technical concepts into value propositions and storytelling for diverse stakeholders. • Clinical Data Literacy: Familiarity with EHR data, Claims, or population health datasets. • Experience in healthcare technology environments, particularly physician-facing products or work with clinical datasets (EHR, Claims, Population Health datasets). • Sitting for prolonged periods of time. Extensive use of computers and keyboards. Occasional walking and lifting may be required.
Responsibilities
• Partner with business owners to cultivate a shared vision for the problem space, constraints, priorities, and ideal end state, and be able to articulate and advocate for this perspective. • Lead the full lifecycle of ML and LLM platform workstreams—from data ingestion, training, and deployment to ongoing optimization of live user workflows based on performance metrics and feedback. • Work with internal teams and end users to develop a deep understanding of requirements, perform thoughtful solution design, use data to test hypotheses, and support teams throughout execution. • Write detailed user stories for new features, capturing descriptions of business rationale, requirements, and success criteria defined by measurable outcomes. • Develop short- and long-term roadmaps that deliver maximum value with minimum risk and assume ongoing iteration. • Partner with AI Researchers to mature proofs-of-concept into production-ready capabilities; perform exploration of user feedback, healthcare records, and validation data to validate model performance.
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