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.
