Sr. Principal or Principal Software and AI Engineer

of the Job:

• Build AI and Agentic Applications: Architect, build, and deploy AI and agentic powered applications that accelerate protocol generation and clinical trial execution, from study design through operational delivery.
• Collaborate Broadly: Partner with clinicians, medical writers, statisticians, data scientists, clinical operations, and product teams to embed AI into regulated clinical development workflows.
• Shape Architecture: Define scalable, reliable platform architecture for multi-agent orchestration, model and tool integration, and the full LLMOps lifecycle.
• Set Standards: Establish best practices for AI-native development, evaluation, testing, observability, and continuous performance monitoring within a validated SDLC.
• Drive Innovation: Assess frontier and open-weight models, agent frameworks, and tooling, and make sound build, buy, and model sourcing decisions.
• Ensure Compliance: Uphold GxP, data privacy, security, and responsible AI standards, with traceability, auditability, and human oversight built into every system.

Supervisory Responsibilities:  

• Act as tech lead with full ownership of product development, accountable for technical direction, delivery, and quality from concept through production.
• Lead project execution, setting priorities, scope, and timelines for the AI engineering team.
• Mentor AI and software engineers through design and code reviews, reference implementations, and hands-on guidance.
• Lead cross-functional technical workstreams with clinical, medical writing, biostatistics, and clinical operations partners.

Education Required:

Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Engineering, Statistics, or a related technical field. Ph.D.

. Advanced training in clinical, pharmaceutical, or life sciences is a plus.

Foundational Background

• Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Engineering, Statistics, or a related technical field. Ph.D. preferred. Advanced training in clinical, pharmaceutical, or life sciences is a plus.
• Experience: 10+ years of software and AI/ML engineering experience, including 5+ years as a tech lead or principal engineer owning large-scale AI products end to end. Experience in the pharmaceutical or life sciences industry is strongly preferred.
• Track Record: Proven success shipping production-grade AI and agentic applications that deliver measurable scientific or business impact, ideally in a regulated environment.

Core Technical Expertise

• Full-Stack Software Engineering: A strong software engineer first. Deep backend expertise in Python (preferred) and/or .NET, with a track record of architecting and building enterprise applications end to end across frontend, APIs, databases, and deployment. Hands-on experience with relational, search, and graph data stores such as PostgreSQL, Elasticsearch, and Neo4j. Sound judgment in system design, data modeling, and production operations.
• Multi-Agent, AI-Native Applications: Deep, hands-on experience architecting and shipping multi-agent applications to production, with strong command of agent harness design, context engineering, orchestration, tool integration (including MCP), and human-in-the-loop controls.
• Frontier Model Application: Strong judgment in selecting and applying frontier and open-weight models, using structured outputs, guardrails, and validation to ensure stable, robust model behavior. Experience with fine-tuning or post-training is a plus.
• Knowledge Engineering: Proven ability to design and manage enterprise knowledge architecture, including ontology design, knowledge graphs, and retrieval, to make domain knowledge structured, governed, and usable by AI systems.
• Evaluation and Reliability: Proven ability to build multi-layered evaluation, from expert benchmarks and rule-based checks to human review and model-based grading, backed by production monitoring.
• AI-Assisted Development: Fluency with AI coding tools and practices, applied within a disciplined SDLC with rigorous testing, code review, and CI/CD.
• Platform and Infrastructure: Strong knowledge of LLMOps, secure cloud deployment (AWS, Azure, or GCP), and observability.

Clinical Development Domain

• Protocol and Trial Execution: Experience with clinical protocol development, study design, or clinical trial operations. Familiarity with ICH M11 structured protocols, CDISC standards, or USDM is a strong plus.
• Regulated AI: Working knowledge of GxP, computer system validation, and emerging regulatory guidance on AI in drug development.

Leadership and Collaboration

• Proven ability to act as tech lead, owning product direction and delivery while mentoring senior engineers.
• Strong partnership with clinicians, medical writers, statisticians, and clinical operations, translating complex AI concepts for research, regulatory, and executive audiences.
• Deep commitment to data privacy, security, and responsible AI in development and deployment.

Travel:  10%