Director, Build Engineer
About the role
This role owns hands‑on software engineering delivery for the Medical Affairs AI Acceleration portfolio, turning solution architecture and product requirements into working, production‑grade AI systems. It is a deeply technical individual‑contributor position accountable for designing components, writing, testing, and shipping code to production. Decisions about feature implementation, AI‑generated code verification, and hardening validated prototypes into scalable systems directly determine the reliability and velocity of the AI systems Medical Affairs depends on. The role carries individual technical authority over its assigned build area, reporting to the Senior Director, Engineering alongside two peer Build Engineers and the Data Integration Engineer. It partners closely with Solution Architecture to implement architectural blueprints, with the Data Integration Engineer to incorporate data from various sources, and with Product Management and UX/Experience Design to translate product and workflow requirements into working software solutions. Solutions are delivered through the full life cycle — proofs of concept, MVPs, and production‑grade systems.
Responsibilities
- Full-Stack & AI System Build: Build, test, and ship production‑grade software across the stack, working in whatever language or framework the problem requires; integrate Agentic AI, LLM‑based capabilities, and RAG pipelines into Medical Affairs products; take validated prototypes and MVPs through to production‑grade systems applying the quality gates set by Solution Architecture and the Senior Director, Engineering; write and maintain code that meets the team's engineering standards for quality, maintainability, and reuse; document code, APIs, and system design decisions clearly and consistently.
- AI-Augmented Development & Verification: Use AI‑augmented development tools while applying rigorous verification and review to all AI‑generated code before it ships; apply LLM integration patterns (prompt design, multi‑provider/model considerations, RAG) to build features that depend on generative AI capabilities; monitor and troubleshoot AI/ML components in production, partnering with Solution Architecture on model performance, drift, and reliability; design and optimize LLM and agent interactions for token efficiency — balancing prompt design, context window usage, and model selection to control cost and latency without sacrificing output quality.
- Data & Systems Integration: Partner with the Data Integration Engineer to ensure builds are backed by reliable, well‑governed data pipelines; integrate with core enterprise systems (e.g., Veeva CRM, PromoMats, Salesforce Life Sciences/Marketing Cloud) as required by product and architecture specifications; identify and resolve technical debt in owned build areas, balancing delivery speed against long‑term system health.
- Cross-Functional Delivery Partnership: Partner with Solution Architecture to implement architectural blueprints and flag design issues discovered during build; partner with Product Management and UX/Experience Design to translate product and workflow requirements into working software, surfacing technical trade‑offs early; participate in sprint and iteration ceremonies with the Scrum Master, delivering committed work reliably.
- Continuous Improvement: Maintain and grow deep, hands‑on technical expertise across relevant languages, frameworks, and AI/ML tooling; contribute to engineering documentation, code review practice, and knowledge‑sharing that scales beyond the immediate build area; bring emerging engineering and AI‑development practices into the team's day‑to‑day work.
Requirements
- Demonstrates a breadth of diverse leadership experiences and capabilities, including the ability to influence and collaborate with peers, develop and coach others, oversee and guide the work of other colleagues to achieve meaningful outcomes and create business impact.
- Bachelor's degree in Computer Science, Engineering, or a related field.
- 8+ years of hands‑on software engineering experience.
- Demonstrated experience building and shipping production software across the full stack, with the ability to work across multiple languages and frameworks as needed.
- Experience integrating AI/ML or LLM‑based capabilities into production systems.
- Experience taking prototypes/MVPs through to production‑grade, scalable systems.
- Experience with modern cloud architecture (AWS or Azure), API‑first design, and CI/CD practice.
- Experience with Agile/Scrum delivery frameworks.
- Strong verification and code‑review discipline, including review of AI‑generated code.
- Experience designing and optimizing LLM/agent prompts and workflows for token efficiency, balancing cost, latency, and output quality at production scale.
- Strong written communication skills, with a track record of producing clear, current technical documentation for code, APIs, and system design decisions.