Sr. Principal Software and AI Engineer

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Sr. Principal Software and AI Engineer based in the United States.

Join a forward-thinking team developing advanced AI-powered applications that transform drug development and clinical trial execution. In this senior technical leadership role, you will shape AI engineering strategy while remaining hands-on in designing, building, and deploying production-grade software. You will lead the development of intelligent, multi-agent systems that streamline clinical protocol generation and improve research workflows. Working closely with clinical experts, medical writers, statisticians, and data scientists, you will translate complex scientific challenges into scalable AI solutions. You will establish engineering standards for reliability, evaluation, security, and regulatory compliance within a validated software development lifecycle. This is an opportunity to influence enterprise AI architecture, mentor engineering talent, and accelerate innovation in a highly regulated, mission-driven environment.

Accountabilities

• Lead AI application development: Architect, develop, and deploy production-ready AI-powered and agentic applications that support clinical protocol generation, study design, and clinical trial operations.
• Define technical strategy and architecture: Establish scalable and reliable architectures for multi-agent orchestration, model integration, tool connectivity, and the complete LLMOps lifecycle.
• Drive end-to-end product delivery: Take ownership of technical direction, project execution, priorities, scope, timelines, and software quality from initial concept through production deployment.
• Establish AI engineering standards: Define and implement best practices for AI-native development, testing, evaluation, observability, continuous monitoring, and CI/CD within a rigorous software development lifecycle.
• Advance AI capabilities: Evaluate emerging foundation models, open-weight models, agent frameworks, and development tools to make informed decisions about technology selection, model sourcing, and build-versus-buy strategies.
• Develop enterprise knowledge systems: Design and maintain knowledge architectures, including ontologies, knowledge graphs, and retrieval systems that make complex scientific information structured, governed, and accessible to AI applications.
• Ensure AI reliability and compliance: Implement evaluation frameworks combining expert benchmarks, rule-based validation, human review, and model-based assessments. Integrate data privacy, security, auditability, traceability, responsible AI practices, and human oversight into production systems.
• Collaborate across disciplines: Partner with clinical, medical writing, biostatistics, data science, product, and clinical operations teams to identify opportunities and integrate AI into regulated clinical development workflows.
• Mentor and develop engineering talent: Guide engineers through architecture discussions, code reviews, reference implementations, technical coaching, and hands-on collaboration while promoting engineering excellence.
• Improve operational performance: Apply AI-assisted development tools and modern software engineering practices to increase developer productivity without compromising testing rigor, maintainability, or production quality.

Requirements

• Education: Bachelor’s degree with at least 7 years of relevant experience, a master’s degree with at least 5 years of experience, or a Ph.D. with at least 3 years of experience.
• Professional experience: Extensive software engineering and AI/ML engineering experience, including principal-level or technical leadership responsibilities and ownership of large-scale AI products from concept through deployment.
• Production AI expertise: A demonstrated track record of delivering reliable, production-grade AI and agentic applications with measurable scientific or business impact. Experience in pharmaceutical, biotechnology, life sciences, or other regulated industries is strongly preferred.
• Full-stack software engineering: Strong backend development expertise in Python and/or .NET, with experience building enterprise applications across frontend interfaces, APIs, databases, and deployment environments.
• Data architecture: Hands-on experience with relational, search,…