- 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.