Director of AI Engineering

Experience, Skills & Accomplishments

Minimum 15+ years of experience in software engineering, including 5+ years in senior engineering leadership roles (Senior Director / Director). Proven experience leading leaders of leaders across complex, multi-product product engineering organizations. Strong technical background across the full software stack, including cloud-native, distributed, and data-intensive systems. Demonstrated experience delivering production-grade Generative AI and Agentic AI solutions, including: LLM-powered applications and services Agentic workflows and orchestration frameworks Model integration, evaluation, and lifecycle management MLOps / LLMOps practices Experience partnering with Data Science and AI Research teams to operationalize AI at scale. Prior experience working in a product-focused software company. Strong executive communication and stakeholder management skills It would be great if you also had

Experience building data and AI platforms using proprietary and third-party datasets in regulated environments. Background in Life Sciences & Healthcare or other highly data-intensive, regulated domains. Experience with responsible AI, data governance, and compliance frameworks.

Track record of driving

enterprise-scale software engineering or AI transformation initiatives

What You Will Be Doing

AI & Software Engineering Platform Leadership

Lead engineering strategy and execution for data and software platforms aligned to AI-driven products across multiple Market Access solutions. Drive the design and delivery of AI-first architectures, including LLM-powered services, agentic workflows, orchestration layers, and human-in-the-loop systems. Build robust data and software foundations that enable advanced analytics, AI inference, and real-time decisioning at scale. Partner with Data Science, AI Research, and Architecture teams to operationalize models into reliable, compliant, and enterprise-grade production systems. Establish platform capabilities for prompt management, model evaluation, observability, governance, and responsible AI.

Organizational & Engineering Leadership

Lead and scale multiple Director- and Senior Manager–led engineering organizations delivering both AI-enabled and core product capabilities. Set clear expectations for end-to-end ownership across full stack, data, and AI-enabled engineering teams. Balance rapid AI innovation with enterprise-grade standards for reliability, security, performance, and maintainability.

Technical & Platform Strategy

Influence and define enterprise standards for software engineering excellence and AI-enabled development, including architecture, coding standards, testing, CI/CD, DevOps/SRE, MLOps, LLMOps, and agent lifecycle management. Ensure platforms are cloud-native, scalable, secure, and compliant with data privacy, regulatory, and governance requirements. Drive adoption of AI-assisted development tools to improve engineering productivity and quality.

Product & Business Partnership

Act as a senior technology partner to Product and Business leaders across Market Access and LS&H portfolios. Translate complex business and customer problems into scalable data, software, and AI solutions with measurable commercial and customer impact. Guide prioritization decisions by balancing innovation, technical debt, feasibility, risk, cost, and time-to-market.

People, Culture & Talent

Build, mentor, and retain a strong leadership bench across Directors and Senior Managers with expertise in product engineering, data platforms, and AI-enabled systems. Shape hiring strategies to attract senior Full Stack, Data, and AI Platform engineering talent. Foster a culture of engineering excellence, accountability, continuous learning, and responsible innovation.

Operational Excellence & Governance

Establish metrics and governance across software, data, and AI platforms covering quality, reliability, cost, performance, security, and business impact. Reduce operational risk through disciplined engineering practices, observability, and continuous improvement. Partner with Security, Legal, Compliance, and Privacy teams to ensure responsible, ethical, and compliant AI deployment.