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

- 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.