AI Architect – Life Sciences

WHAT YOU'LL DO

• Own the architecture, and make it easy to build on.
• Own and evolve our enterprise reference architecture for generative and agentic AI across models, retrieval, orchestration, reasoning, and the governance that runs through all of it.
• Build and demonstrate, hands-on.
• Prototype in code, stand up working proofs and demos, and stay close to the models and tools leading by showing, and learning continuously as the field moves.
• Build reusable frameworks, blueprints, and paved-road patterns that engineering and product teams adopt.
• Lead solutioning from proof-of-concept to production: scope experiments, prove them, and build the roadmap to scale.
• Stand up the shared AI platform and enablement model serving, vector and graph retrieval, tool and agent orchestration, observability, and evaluation and help teams build on it safely.
• Embed governance by design: traceability, human-in-the-loop, evaluation, and tenant isolation built into every architecture from the start.
• Own build-vs-buy decisions and set technical standards; run architecture reviews and mentor engineers.
• Translate technical trade-offs clearly for leadership, clients, and engineering.

WHAT YOU'LL BRING

• Production-proven, end to end.
• Deeply hands-on you still build.
• 8+ years in software / AI engineering, with production-proven delivery of agentic, generative, and classical ML/DL systems not just POCs.
• Hands-on depth across the modern agentic stack: orchestration frameworks (e.g.
• LangGraph, Dapr Agents), protocols (MCP; familiarity with A2A), RAG and GraphRAG, vector and graph databases (e.g. Neo4j, pgvector), model serving (e.g.
• vLLM), and LLMOps (versioning, drift, cost, evaluation).
• Practical evaluation and guardrails experience making model quality measurable and defensible.
• Command of the full span from classical ML/DL to generative to agentic, and the judgment to choose the simplest approach that works.
• Cloud and data-platform depth (AWS / Azure / Databricks) and familiarity with semantic layers and lakehouse architectures.
• End-to-end architecture ownership and the ability to set and evolve standards across an organisation.
• Excellent communication and stakeholder management, including with senior leadership, and a track record of mentoring.