Executive Director - High Velocity Engineering Platforms

Labcorp•Durham, NC

1d•Hybrid

About The Position

Labcorp is seeking an Executive Director, High Velocity Engineering Platforms, to lead enterprise AI engineering platform strategy, architecture, and delivery. The role is based in Durham, North Carolina, with a hybrid work arrangement, and reports to the Senior Vice President of Platforms, AI & High Velocity Engineering. Travel is 40–50%. The position leads the development of scalable, secure, reliable, and cost-effective AI capabilities across the organization, including engineering frameworks, developer experience, integrations, AI controls, observability, data science and AI engineering, and agentic AI practices. It also sets platform roadmaps, investment priorities, performance measures, and technology strategies that deliver business value. The Executive Director partners with Technology, Product, Data, Security, Privacy, Legal, and business teams to promote adoption, organizational change, governance, and compliance. The role provides executive leadership for engineering organizations, including team development, budget and resource management, KPI-setting, and representation of platform strategy to senior leadership. It also evaluates emerging technologies and advances operational excellence across technical debt management, reliability engineering, DevOps/SRE practices, performance, security, and continuous improvement.

Requirements

Bachelor's degree.
15 or more years of progressive experience in data, analytics, cloud, technology, platform engineering, AI, or digital transformation, including leadership of large-scale enterprise technology initiatives.
10 or more years leading multidisciplinary teams, with responsibility for organizational strategy, execution, operational performance, talent development, and budget management.
10 or more years defining and executing enterprise data, analytics, AI, cloud, or technology transformation strategies within large, complex, or highly regulated organizations.
10 or more years leading enterprise-scale modernization initiatives involving cloud platforms, data ecosystems, analytics, interoperability, AI/ML capabilities, and mission-critical technology services.
10 or more years partnering with executive and C-suite stakeholders to align technology strategy and investments with business priorities, lead organizational change, manage risk, and deliver measurable business outcomes.
10 or more years of experience with data governance, privacy, security, regulatory compliance, and responsible AI principles within healthcare or other highly regulated environments.
Expert-level proficiency across multiple programming languages, such as Python, Go, Java, and TypeScript, with the ability to understand, design, review, and provide strategic direction on complex codebases.
Advanced knowledge of production AI systems, including model deployment, inference optimization, lifecycle automation, scalability, reliability, and operational effectiveness.
Ability to communicate complex technical strategies, recommendations, risks, and trade-offs to executive, technical, and business stakeholders and drive alignment across functions.
Strong understanding of developer and business user needs, with a product-oriented approach to platform design and measurable business value.
Advanced knowledge of agentic AI design, orchestration patterns, lifecycle management, safety controls, and responsible development and operation of agent-based solutions.
Advanced knowledge of prompt engineering, system design, and practices supporting reliable, controlled, and effective AI solutions.
Ability to design and guide large-scale, modular platform architectures balancing scalability, extensibility, performance, reliability, and security.
Advanced knowledge of cloud platforms, managed services, cost optimization, and hybrid deployment models for enterprise-scale solutions.
Ability to evaluate emerging technologies and industry trends, assess opportunities and risks, and translate insights into platform strategies and investment priorities.

Nice To Haves

MBA or degree in Computer Science, Data Science, Engineering, Information Technology, or Business Administration.
10 or more years of experience in healthcare, diagnostics, life sciences, pharmaceutical, or other highly regulated industries.

Responsibilities

Lead the strategy, architecture, and delivery of enterprise AI engineering platforms that provide scalable, secure, reliable, and cost-effective AI capabilities.
Define and govern engineering frameworks, standards, tooling, libraries, CI/CD pipelines, runtime requirements, and developer experience patterns.
Establish developer experience capabilities, including reusable frameworks, templates, SDKs, and integrated workflows.
Lead enterprise integration capabilities, including APIs, connectors, and standards for interoperability across applications, data platforms, AI services, and third-party technologies.
Architect and govern AI control plane capabilities, including policy enforcement, access management, model lifecycle controls, model provenance, security requirements, and operational controls.
Establish enterprise observability and operational standards, including Open Telemetry, service-level objectives, real-time monitoring, distributed tracing, dashboards, and incident response practices.
Enable scalable data science and AI engineering capabilities for experimentation, model deployment, feature management, inference, and production operations.
Advance agentic AI engineering practices through lifecycle patterns, orchestration approaches, safety controls, and governance frameworks.
Develop and execute AI engineering and platform roadmaps, investment priorities, performance metrics, cost transparency, and technology strategies.
Partner across Technology, Product, Data, Security, Privacy, Legal, and business functions to drive adoption, organizational change, governance, and compliance.
Provide executive leadership for engineering organizations, including building and developing teams, managing budgets and resources, establishing KPIs, and representing platform strategy with senior leadership.
Evaluate emerging AI technologies, industry capabilities, opportunities, and risks and translate them into platform investments and roadmap recommendations.
Promote operational excellence through technical debt management, reliability engineering, DevOps/SRE practices, platform performance, security, and continuous improvement.