Technical Architect - AI Platforms

THE ROLE

You define end-to-end architecture for Incedo's AI-powered enterprise products — applications, APIs, data, security and infrastructure — and you make it hold up across cloud, scale and audit.

You work directly with Product and engineering leadership, chair architecture governance, and act as the technical authority in high-stakes client conversations.

WHAT YOU'LL OWN

1. Architecture and blueprints

• Define target architecture for enterprise AI platforms: services, APIs, data stores, security model, and deployment topology.
• Produce solution blueprints that engineering can build from and that clients can assure against.
• Design for scale, resilience and multi-tenancy across distributed, cloud-native systems.

2. AI platform architecture

• Architect the AI layer — LLM and RAG serving, agent orchestration, vector stores, and the line between model-driven and deterministic behaviour.
• Define AI infrastructure patterns: inference cost and latency, model routing, caching, evaluation hooks, observability.
• Set the guardrail and data-handling architecture that makes AI deployable in regulated environments.

3. Integration and governance

• Define integration patterns with enterprise platforms — identity, core systems, data pipelines, event backbones.
• Lead architecture governance: review forums, standards, technical debt calls and architecture compliance across teams.
• Drive technology evaluations, POCs and build-versus-buy calls on evidence rather than preference.

4. Engagement and influence

• Partner with Product and Engineering leadership to keep architecture aligned to roadmap and delivery reality.
• Represent the architecture to client CTOs, security teams and auditors, and support pre-sales and RFPs.

WHAT YOU'LL BRING

• 12–16 years in software engineering, including 5+ years in solution or platform architecture.
• Large-scale distributed systems designed and shipped — with the scars to show what fails at scale.
• Deep microservices and API architecture: contracts, versioning and integration patterns.
• Strong cloud architecture on Azure, AWS or GCP, with containerisation and Kubernetes in production.
• DevSecOps fluency: security by design, secrets management, supply chain and CI/CD pipelines.
• Non-functional rigour: performance, availability, observability, cost and compliance.
• Decision discipline — able to write and defend an architecture decision record, not just a diagram.

GOOD TO HAVE

• AI-powered enterprise applications architected and taken to production.
• Hands-on exposure to LLMs, RAG, AI agents and GenAI platforms.
• AI orchestration frameworks, vector databases and AI infrastructure design.
• TOGAF, Azure Solutions Architect Expert or AWS Solutions Architect Professional certification.