Opportunity For AI Architect _ Excelra

We are looking for an AI Architect for rebuilding AI on top of real regulated life-sciences data where "close enough" doesn't survive a second question from an FDA or EMA reviewer.

Experience Deeply hands-on • 8+ years in production

THE ROLE

Shape the architecture behind AI at Excelra.

Were hiring two AI Architects to shape the architecture behind AI at Excelra and turn it into production systems across the company. Reporting to the Head of AI, you’ll own our reference architecture for AI in life sciences, drive AI enablement across teams, and set the standards others build on.

This is a hands-on-keyboard, decision-making role for someone who builds, prototypes, and demonstrates — who has deployed agentic, generative, and classical AI in production, and keeps learning as the field moves. You know what survives contact with real users and real audits.

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.

NICE TO HAVE

• Regulated-industry experience — 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 graphs, semantic layers, and ontologies as first-class design elements.
• Multi-tenant SaaS or customer-deployed system design.

Public contributions — talks, writing, or open source — on generative / agentic AI architecture.