Sr Product Manager (AI Platform)

Evolent

1d•$115,000 - $135,000

About The Position

Evolent is building a new AI platform from the ground up, and this role builds it. The architecture is still being shaped, the first capabilities are moving into production now, and the company is backing the platform as a central piece of its strategy with dedicated product, engineering, applied AI, and data science teams and standing visibility to the executive team. As Sr Product Manager (AI Platform), you will own one of the shared platform workstreams that every AI capability across the company is built on, working shoulder-to-shoulder with architects, applied AI engineers, data scientists, and clinical and operations leaders. Come be part of a mission-driven team building a transformational piece of technology to change how Healthcare is delivered!

Requirements

Bachelor's degree in a relevant field (e.g., Computer Science, Data Science, Engineering, Business, Healthcare Administration) or equivalent practical experience.
Minimum 5-7+ years of experience in product management, with a strong emphasis on software products in the healthcare technology sector, including 2+ years owning platform, infrastructure, data, or API products consumed by other engineering teams.
Proven track record of successfully launching and managing complex software products.
Demonstrated ability to define and execute product strategy, roadmap, and go-to-market plans.
Working knowledge of modern AI technologies and the trade-offs among them: model selection, prompting and orchestration approaches, retrieval-augmented generation, fine-tuning versus prompting, and agentic patterns.
Proven ability to partner with architects and engineers on technical trade-offs, including build versus buy versus partner, canonical data models, and API and event contracts.
Experience delivering software in a regulated environment with sensitive or protected data (PHI/PII), including audit, traceability, and access-control requirements.
Strong analytical skills with the ability to interpret data, identify trends, and make data-driven decisions.
Excellent communication, presentation, and interpersonal skills, with the ability to effectively communicate with technical and non-technical stakeholders.
Experience with Agile development methodologies (e.g., Scrum, Kanban) and proficiency in product management tools (e.g., JIRA, Confluence, Aha!).
Ability to thrive in a fast-paced, dynamic environment and manage multiple priorities effectively.

Nice To Haves

Master's degree in a relevant field (e.g., Business, Computer Science, Data Science, Healthcare Administration, Engineering).
Deep understanding of the healthcare industry, including payer, provider, and value-based care models; experience in prior authorization, utilization management, appeals, or payer and provider clinical operations is a plus.
Direct experience leading GenAI or machine learning product initiatives from concept through launch, and bringing AI-enabled products to enterprise scale.
Working knowledge of healthcare interoperability standards such as FHIR, X12 278, and HL7, and of CMS Interoperability and Prior Authorization rules.
Depth in Microsoft Azure as a primary platform (e.g., AKS, Service Bus, Azure SQL and Cosmos DB, Azure AI Foundry, Azure OpenAI, Document Intelligence, AI Search). Comparable AWS or Google Cloud depth considered.
Familiarity with event-driven architecture patterns (CloudEvents, event sourcing, decision ledgers and audit trails) and with rules engines delivered as shared services.
Experience with agent architectures and orchestration frameworks, embedding and retrieval pipelines, document processing (OCR to NLP to embedding to search), and model routing through a centralized AI gateway.
Knowledge of MLOps and LLMOps practices, including evaluation datasets and scoring, shadow-mode validation, drift and quality monitoring, and observability tooling such as OpenTelemetry.
Exposure to identity, access, and lifecycle governance for AI systems and agents, and to HITRUST or SOC 2 certification programs.
Familiarity with data warehouse and analytics technologies such as SQL, Python, Power BI, Tableau, Databricks, or Snowflake.
Experience collaborating with data scientists on experimentation, model tuning, and deployment strategies.

Responsibilities

Lead the product lifecycle for your platform workstream from strategic planning to tactical activities, including ideation, discovery, development, launch, and post-launch analysis.
Define and articulate the product vision, strategy, and roadmap for your workstream, ensuring alignment with the overall AI platform roadmap, Evolent's company goals, and market trends.
Conduct thorough market research, competitive analysis, and customer feedback gathering (internal teams and clients) to identify unmet needs and opportunities.
Translate complex business requirements and user needs into detailed product specifications, user stories, and acceptance criteria.
Collaborate closely with engineering, architecture, applied AI, data science, design, and other cross-functional teams to ensure successful product development and delivery.
Prioritize the product backlog and manage feature development, ensuring optimal resource allocation and timely delivery against platform and capability delivery dates.
Develop and execute go-to-market and adoption strategies, including positioning, messaging, and enablement materials for the internal teams and clients who consume your platform components.
Monitor product performance, analyze key metrics, and identify areas for improvement and iteration.
Act as a product evangelist, communicating product value and benefits to internal and external stakeholders.
Mentor and provide guidance to junior product managers and team members.
Partner with data science and engineering leaders to define scalable AI architectures, model evaluation frameworks, and production readiness criteria.
Own definition, execution, and measurement of proof-of-concepts and pilot initiatives to validate emerging technologies.
Drive decisions regarding AI feature prioritization based on customer value, operational efficiency, and technical feasibility.