VP, AI Enablement & Agent Platform
Datavant is the data collaboration platform trusted for healthcare. Guided by our mission to make the world's health data secure, accessible and actionable, we provide critical data solutions for organizations across the healthcare ecosystem - including providers, health plans, researchers, and life sciences companies. From fulfilling a single patient's request for their medical records to powering the AI revolution in healthcare, Datavanters are building the future of how data is connected and used to improve health.
By joining Datavant today, you're stepping onto a driven and highly collaborative team that is passionate about creating transformative change in healthcare.
Role Overview
The VP of AI Enablement & Agent Platform owns the infrastructure, standards, and organizational enablement required to deploy AI agents at enterprise scale. This includes agent hosting and orchestration infrastructure, MCP server architecture, reusable AI components and reference architectures, and the engineering standards that govern how agents are built, deployed, and operated in production.
This is distinct from Datavant's ML Platform organization, which owns model training, feature stores, and data science infrastructure. This role owns the application layer, the infrastructure and standards that turn models into production agent systems.
This is not a strategy-only position. The VP is expected to build and ship platform infrastructure, make definitive architectural decisions, and lead a team capable of operating critical AI infrastructure in a regulated healthcare environment.
Key Responsibilities
AI Agent Infrastructure & Orchestration
Own the design, build, and operation of Datavant's AI agent hosting and orchestration platform, including inference infrastructure, agent runtimes, compute provisioning, and API gateway services
Architect multi-agent orchestration capabilities that support the full autonomy spectrum, from human-in-the-loop copilots to fully autonomous workflow agents, with appropriate controls at each level
Own agent lifecycle management: versioning, deployment, rollback, monitoring, and decommissioning of production agents
Establish infrastructure patterns that decouple agent logic from underlying model providers, enabling model portability and vendor flexibility
Drive cost optimization for inference at scale as agent usage grows across the organization
MCP Server Architecture & AI Integration
Own the MCP server architecture that connects AI agents to Datavant's enterprise systems, data stores, and external services
Design and enforce standardized integration patterns, tool catalogs, connector frameworks, and authentication models, that allow agents to interact with enterprise systems securely and consistently
Build reusable MCP servers and integration components that reduce time-to-production for new AI use cases from weeks to days
Establish integration testing, security review, and certification processes for AI system connectors, ensuring agents cannot access systems or data beyond their authorized scope
Partner with infrastructure and security teams to define the boundary between agent-accessible and agent-restricted systems, particularly for environments handling PHI and other regulated data
AI Developer Experience & Enablement
Own the internal developer experience for AI — SDKs, CLIs, documentation, templates, reference architectures, and golden paths that make it straightforward for any engineering team to build on the AI platform
Build and maintain a catalog of reusable AI components including agent templates, prompt libraries, evaluation frameworks, and production-ready patterns, that accelerate adoption and reduce duplicated effort
Establish engineering standards for agent development, testing, and production operation, including security requirements, performance benchmarks, and quality gates
Lead enablement programs from workshops and office hours to embedded engineering support for high-priority AI initiatives
Define and track adoption metrics that measure platform utilization, developer productivity impact, and time-to-production
Serve as the central coordination point for AI engineering practices by consolidating duplicated efforts and promoting proven approaches through the platform rather than mandates
AI Governance & Production Operations
Establish a multi-layered AI governance framework spanning build-time (secure agent construction and review), deployment (approval gates, environment controls, access scoping), and runtime (behavioral monitoring, anomaly detection, kill switches)
Partner with Security, Legal, Compliance, and Privacy to embed responsible AI practices by design, ensuring agents operate within HIPAA, SOC 2, and FedRAMP readiness requirements
Define and enforce agent permissioning models with scoped data and system access and audit trails for every action
Establish production SRE practices for AI systems including incident response, reliability targets, capacity planning, and on-call responsibilities
Cross-Functional Partnership
Partner with the VP of Data & AI and ML Platform team to establish clean ownership boundaries — ML Platform owns model training and data science tooling; AI Enablement owns the agent application layer, integration infrastructure, and production agent operations
Work with business unit leaders to identify and prioritize high-impact AI agent use cases, translating business needs into platform capabilities
Advise senior leadership on AI agent technology strategy such as build-vs-buy decisions, vendor selection, emerging standards, and organizational readiness
Requirements
15+ years in technology leadership, with at least 5 years in a VP or senior director role leading platform engineering, developer infrastructure, or AI/ML infrastructure organizations
Demonstrated experience building and operating production AI/ML systems at enterprise scale, not just research or experimentation, but systems with SLAs, on-call, and real operational accountability
Deep architectural expertise in distributed systems, API platform design, and infrastructure engineering
Strong understanding of the AI agent technology landscape — LLMs, agent frameworks, RAG architectures, MCP, tool-use patterns, and multi-agent orchestration
Experience establishing engineering standards and governance frameworks that achieve compliance without killing developer velocity, particularly in regulated environments (healthcare, life sciences, or financial services)
Track record building developer platforms or internal tooling organizations where adoption is earned through quality, not mandated
Demonstrated ability to operate as a peer-level leader and credible with engineers, effective with executives, capable of making definitive architectural decisions under ambiguity
Strong cost management instinct and able to balance platform investment against operational efficiency, particularly around inference compute costs that scale with adoption
Nice To Have
Direct experience building AI agent platforms or agentic systems in production, including agent hosting, orchestration, MCP server development, or multi-agent coordination
Background in platform engineering or internal developer experience, building tools, SDKs, and infrastructure that other engineering teams consume as a service
Experience with healthcare data environments, HIPAA compliance, and deploying AI in regulated healthcare settings
Familiarity with AI governance and responsible AI frameworks, agent auditability, explainability, and enterprise compliance models for autonomous systems
Experience managing AI/LLM infrastructure costs at scale, inference optimization, model routing, cost attribution, and chargeback models
Background in security engineering or security-conscious infrastructure design
We are committed to building a diverse team of Datavanters who are all responsible for stewarding a high-performance culture in which all Datavanters belong and thrive. We are proud to be an Equal Employment Opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, disability, veteran status, or other legally protected status.
At Datavant our total rewards strategy powers a high-growth, high-performance, health technology company that rewards our employees for transforming health care through creating industry-defining data logistics products and services.
The range posted is for a given job title, which can include multiple levels. Individual rates for the same job title may differ based on their level, responsibilities, skills, and experience for a specific job.
The estimated total cash compensation range for this role is
$270,000—$315,000 USD
To ensure the safety of patients and staff, many of our clients require post-offer health screenings and proof and/or completion of various vaccinations such as the flu shot, Tdap, COVID-19, etc. Any requests to be exempted from these requirements will be reviewed by Datavant Human Resources and determined on a case-by-case basis. Depending on the state in which you will be working, exemptions may be available on the basis of disability, medical contraindications to the vaccine or any of its components, pregnancy or pregnancy-related medical conditions, and/or religion.
This job is not eligible for employment sponsorship.
Datavant is committed to a work environment free from job discrimination. We are proud to be an Equal Employment Opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, disability, veteran status, or other legally protected status. To learn more about our commitment, please review our EEO Commitment Statement here (https://www.datavant.com/eeo-commitment-statement) . Know Your Rights (https://www.eeoc.gov/know-your-rights-workplace-discrimination-illegal) , explore the resources available through the EEOC for more information regarding your legal rights and protections. In addition, Datavant does not and will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay.
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