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About This Role
Labcorp is a global leader in laboratory services, providing the insights and answers that help healthcare providers, patients, researchers, pharmaceutical companies and health systems make confident decisions and improve outcomes. Through our unparalleled science, data, technology and laboratory network, we advance diagnostics, accelerate innovation and help address some of the world’s most important health challenges. As we shape the future of healthcare, we are leveraging advanced technologies, intelligent digital solutions and data\-driven innovation across our operations to enhance how work gets done and deliver greater value to customers and patients. With our global scale and deep expertise, you’ll have the opportunity to do meaningful work, grow your career and make a real impact on people’s health around the world. Together, we’re improving health and improving lives.
Labcorp is seeking an Executive Director, Chief AI Architect, to join our team at Durham, NC.
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Work Schedule: Work Schedule: Durham, NC / Hybrid \| Full\-time \| Reports to: Chief AI Officer \| Travel: 40\-50%
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Job Responsibilities
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- Define and lead Labcorp's enterprise AI architecture strategy, standards, and roadmap.
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- Drive AI architecture decisions across transformation programs, corporate functions, and enterprise platforms to maximize business value, scalability, and sustainability.
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- Partner with business, product, engineering, data, and enterprise architecture leaders to embed AI capabilities into key initiatives.
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- Establish and govern enterprise AI reference architectures, reusable patterns, and best practices for responsible AI adoption.
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- Lead evaluation of emerging AI technologies, platform investments, and build\-versus\-buy decisions.
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- Ensure AI solutions meet requirements for security, governance, quality, observability, and regulatory compliance.
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- Measure and optimize AI outcomes including solution quality, adoption, operational efficiency, and cost effectiveness.
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- Build and lead a high\-performing AI architecture organization while developing AI architecture capabilities across the enterprise.
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Minimum Qualifications
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- Bachelor's degree.
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- 15 or more years of experience in enterprise architecture, software architecture, AI/ML architecture, or technology leadership roles.
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- 5 or more years of experience in healthcare, diagnostics, life sciences, pharmaceutical, payer, provider, or other regulated industries supporting solutions involving PII, PHI, compliance, and audit requirements.
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- 5 or more years of hands\-on AI experience, including production LLM, agentic, RAG, or AI\-assisted development capabilities, with direct experience designing and shipping agentic systems in production
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Preferred Qualifications
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- Master’s degree in computer science, Artificial Intelligence, Data Science or Engineering
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- 5 or more years of experience designing and implementing enterprise AI platforms, agentic AI systems, retrieval\-augmented generation (RAG), and multi\-agent architectures.
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- 5 or more years of experience with AI orchestration frameworks, agent protocols, vector databases, and multi\-model AI ecosystems.
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- 5 or more years of experience with enterprise knowledge graph platforms, semantic data models, ontologies, or semantic layer architectures.
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Additional Job Standards
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- Demonstrated ability to influence enterprise technology strategy through partnerships with Enterprise Architecture, business transformation, and executive leadership teams.
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- Demonstrated expertise evaluating emerging AI technologies, architectural patterns, and platform investments.
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- Ability to establish and drive adoption of enterprise AI standards, reference architectures, and governance practices.
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- Deep knowledge of modern AI architectures, including foundation models, agentic systems, retrieval\-augmented generation (RAG), orchestration, evaluation frameworks, safety controls, observability, and production operations.
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- Strong understanding of LLMOps practices, including model and agent lifecycle management, AI quality evaluation, observability, and cost optimization.
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- Strong knowledge of security, privacy, identity, and governance requirements for AI solutions in regulated environments, including PII and PHI protections.
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- Demonstrated ability to assess AI architecture tradeoffs based on business value, scalability, operational risk, production quality, and cost considerations.
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- Recognized contributions to the AI architecture community through industry speaking, publications, advisory councils, or professional organizations.
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- Proven ability to build, mentor, and lead high\-performing architecture teams and communities of practice.
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- Strong executive communication and stakeholder management skills across technical and non\-technical audiences.
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Benefits: Medical, Dental, Vision, Life, STD/LTD, 401(k), Paid Time Off (PTO) or Flexible Time Off (FTO), Tuition Reimbursement and Employee Stock Purchase Plan. Employees regularly scheduled to work less than 20 hours, Casual, Intern, and Temporary employees are only eligible to participate in the 401(k) Plan. Employees who are regularly scheduled to work a 7 on/7 off schedule are eligible to receive all the foregoing benefits except PTO or FTO. For more detailed information, please click here.
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Labcorp is proud to be an Equal Opportunity Employer:
Labcorp strives for inclusion and belonging in the workforce and does not tolerate harassment or discrimination of any kind. We make employment decisions based on the needs of our business and the qualifications and merit of the individual. Qualified applicants will receive consideration for employment without regard to race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), family or parental status, marital, civil union or domestic partnership status, sexual orientation, gender identity, gender expression, personal appearance, age, veteran status, disability, genetic information, or any other legally protected characteristic. Additionally, all qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable law.
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Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 4,317 AI roles we're tracking, AI Architect positions make up 1% of the market. At Labcorp, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $237,300 based on 102 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $250,000.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Labcorp AI Hiring
Labcorp has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Based in Durham, NC, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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