Roles and Responsibility

Own the architecture and make it easy to build on.
Develop and evolve our enterprise reference architecture for generative and agentic AI across models, retrieval, orchestration, reasoning, and governance.
Build and demonstrate hands-on prototypes, stand up working proofs, and continuously learn as the field evolves.
Lead solutioning from proof-of-concept to production, including scoping experiments and proving them.
Set technical standards and own build-vs-buy decisions, running architecture reviews and mentoring engineers.
Translate technical trade-offs clearly for leadership, clients, and engineering.

Job Requirements

Production-proven, end-to-end experience in software/AI engineering.
Hands-on depth across the modern agentic stack, including orchestration frameworks, protocols, RAG and GraphRAG, vector and graph databases, model serving, and LLMOps.
Practical evaluation and guardrails experience, making model quality measurable and defensible.
Command of the full span from classical ML/DL to generative to agentic, with the ability to choose the simplest approach that works.
Cloud and data-platform depth (AWS / Azure / Databricks) and familiarity with semantic layers and lakehouse architectures.
Excellent communication and stakeholder management skills, including senior leadership and mentoring.
Experience in regulated industries such as life sciences, pharma, or healthcare, and compliance-aware AI architecture (GxP, GAMP 5, 21 CFR Part 11, ALCOA+ data integrity, EU AI Act / HIPAA awareness).
Knowledge of graphs, semantic layers, and ontologies as first-class design elements.
Multi-tenant SaaS or customer-deployed system design experience.
Public contributions (talks, writing, or open source) on generative/agentic AI architecture.

AI Architect - Life Sciences

Job Description