Director, Enterprise AI Architecture

Clinisys•Morrisville, NC

12h•$150,000 - $180,000•Hybrid

About The Position

As Director, Enterprise AI Architecture & Transformation, you will lead the design, delivery, and scaling of secure, responsible, and business-aligned AI solutions that advance Clinisys’ digital transformation. This is a strategic leadership role—partnering closely with the Office of the CTO, Product & Engineering, Cyber Security, Data, Operations, and business leaders to translate priorities into integrated, end-to-end capabilities that drive speed, experience, and measurable outcomes. You will be accountable for both the architecture and the realization of transformation results. This includes owning the AI roadmap, operating model, and performance metrics to increase automation, reduce cycle times, and enhance user and customer experience. You will lead a small team of AI engineers/architects, influence cross-functional stakeholders, and drive disciplined execution with urgency and strong governance. Building an AI-first organization is central to Clinisys’ purpose and the impact we deliver. As a global provider of intelligent diagnostic informatics solutions, we build AI-enabled, cloud-based platforms to enhance diagnostic workflows across healthcare, life sciences, and public health. By applying intelligent technology thoughtfully and responsibly, we help laboratories and testing environments operate more effectively, generate meaningful insights at scale, and ultimately support healthier and safer communities. Operating across more than 30 countries, Clinisys expects all colleagues, regardless of role or function, to work confidently with AI-enabled tools, apply digital and analytical thinking, and continuously adapt as technologies evolve. We must drive an AI-first sense of purpose and urgency.

Requirements

Proven experience designing, delivering, and scaling AI/ML or GenAI solutions in production environments with measurable business impact
Demonstrated success leading enterprise transformation initiatives that improved automation, reduced cycle times, enhanced user or customer experience, and increased adoption
Experience with MLOps practices, including model lifecycle management, deployment pipelines, monitoring, and value realization governance
Deep experience developing GenAI solutions such as RAG pipelines, orchestration frameworks, automation techniques, and evaluation approaches
Strong understanding of enterprise architecture, business process transformation, and cross-functional operating models
Knowledge of data security, privacy, access controls, and responsible AI principles including bias, drift, transparency, and governance
Strong leadership and influencing skills, with the ability to align stakeholders, drive decisions, and mobilize teams across organizational boundaries
Strong communication skills, with the ability to translate complex technical concepts into clear business recommendations and outcome-oriented plans
Bachelor’s degree in Computer Science, Engineering, Data Science, or related field, or equivalent experience
10+ years of experience in software engineering, enterprise architecture, or machine learning engineering
7+ years of experience leading teams and cross-functional programs in a managerial or director-level capacity
Demonstrated experience owning enterprise roadmaps, influencing senior stakeholders, and delivering measurable transformation outcomes

Nice To Haves

Extensive experience in regulated environments or healthcare/life sciences is a plus

Responsibilities

Lead enterprise AI architecture and digital transformation initiatives aligned to business strategy and enterprise priorities
Act as a proactive transformation driver by identifying high-value opportunities and shaping end-to-end future-state workflows
Own the transformation roadmap, including prioritization, dependencies, and value realization
Influence cross-functional leaders to align priorities, remove barriers, and accelerate execution
Lead the design and implementation of AI/ML and GenAI solutions aligned to enterprise architecture standards and transformation goals
Guide development of scalable solutions including retrieval-augmented generation (RAG) pipelines, orchestration workflows, and enterprise integration patterns
Ensure solutions are secure, resilient, reusable, and enable measurable improvements to business workflows
Partner with Engineering to deliver high-performing, production-ready AI services across enterprise environments
Own measurable outcomes including automation, cycle time reduction, experience improvement, and adoption
Define and track KPIs such as automation rates, turnaround times, user experience metrics, and time-to-value
Establish performance scorecards and lead regular operating reviews to ensure delivery of business outcomes
Ensure initiatives are governed and executed to deliver value realization, not just technical completion
Drive delivery of integrated, end-to-end digital capabilities across Product, Engineering, Data, Operations, and business teams
Establish shared accountability across functions for process, data, and experience improvements
Communicate priorities, risks, and outcomes clearly to technical and executive stakeholders
Embed change management and adoption strategies to ensure successful implementation at scale
Implement enterprise AI architecture standards, MLOps practices, and governance frameworks
Ensure compliance with privacy, security, and responsible AI requirements in partnership with Cyber Security and Compliance
Continuously improve frameworks, tools, and operating models to support scalable transformation