Head of AI & Principal Architect

The Role

We are hiring a Head of AI & Principal Architect to help define and build the next generation of AI systems powering the Pi Health platform. This role will define the long-term AI architecture of the FICS platform and build the initial generation of production AI systems.

This role sits at the intersection of machine learning, large language models, agentic systems, and clinical research infrastructure. You will work closely with engineering, product, and leadership to design and deploy AI capabilities that automate complex workflows across the clinical trial lifecycle. Rather than building isolated models, you will help architect AI-native systems embedded directly into the core of our platform.

You will play a central role in advancing Pi Health's AI roadmap, including:

• Domain-adapted LLM systems for clinical research workflows
• Multi-agent systems that automate operational tasks in trials
• Retrieval-augmented systems for regulatory and medical knowledge
• Intelligent pipelines that extract structured insights from clinical data and documents

What You'll Do

AI Architecture & Strategy

• Define and drive Pi Health's applied AI strategy across the FICS platform
• Identify high-impact opportunities to integrate AI into clinical research workflows
• Design AI system architectures that scale across multiple products and customers
• Evaluate emerging models, tools, and frameworks to keep Pi Health at the forefront of applied AI in life science

LLM Systems & Agentic Workflows

• Design and build LLM-powered systems for clinical trial automation
• Develop multi-agent pipelines using frameworks such as LangGraph, LangChain, CrewAI, or similar orchestration tools
• Build autonomous and semi-autonomous agents for tasks such as:
  • Clinical data extraction
  • Medical coding automation
  • Regulatory document analysis
  • Protocol interpretation

RAG Systems & Knowledge Infrastructure

• Design and implement retrieval-augmented generation architectures
• Develop vector search systems and embedding pipelines for clinical data and documents
• Optimize prompt strategies for reliability and hallucination reduction in regulated contexts
• Build evaluation frameworks for model accuracy and clinical reliability

MLOps & Production AI Systems

• Build and maintain ML/LLM pipelines from experimentation through production deployment
• Implement LLMOps practices including:
  • Model versioning
  • Evaluation pipelines
  • Runtime monitoring
  • Cost optimization
• Integrate AI systems into production infrastructure on AWS

Data Pipelines & Feature Engineering

• Design data pipelines that transform clinical data into AI-ready datasets
• Work with structured EHR data, clinical notes, and regulatory documents
• Collaborate with engineering teams on feature stores and inference pipelines
• Ensure compliance with HIPAA, GxP, and other regulated data standards

Cross-Functional Collaboration

• Partner with product, clinical operations, and regulatory teams to translate real-world problems into AI solutions
• Educate the engineering team on AI tooling and best practices
• Help establish internal AI governance and evaluation frameworks

Qualifications

• Proficiency in Python and familiarity with modern ML frameworks (PyTorch, Hugging Face, etc.)
• Experience building LLM applications including prompt engineering, fine-tuning, or RAG systems
• Experience working with agentic frameworks or orchestration systems
• Familiarity with MLOps / LLMOps including model deployment, evaluation, and monitoring
• Experience with cloud infrastructure for ML workloads (AWS preferred)
• Strong communication skills and ability to collaborate across disciplines
• A builder's mindset—you move quickly, experiment, and ship

Strongly Preferred

• Experience in healthcare, life sciences, or clinical research
• Familiarity with clinical data standards such as CDISC, SDTM, MedDRA, or WHODrug
• Understanding of regulated environments such as GxP, HIPAA, or 21 CFR Part 11
• Experience with vector databases (Pinecone, Weaviate, Qdrant, pgvector)
• Experience with LLM evaluation and observability tools (Langfuse, LangSmith, Weights & Biases)
• Experience optimizing models for production (quantization, distillation, etc.)

Technology Stack

What Makes This Role Unique

• Work on one of the largest unsolved infrastructure problems in healthcare
• Build production AI systems used by global pharmaceutical companies
• Apply LLMs and agentic systems to real-world clinical data and regulatory workflows
• Help define how AI transforms global drug development