Director - AI Software 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 Managerled 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.