AI Platform Architect

Description

The company builds AI products for clients and needs a platform architect to shape the shared capabilities those products run on: building, deploying, securing, monitoring and scaling them across mixed cloud, on-premises and edge setups.

You will join the AI Transformation and Agentic Systems Practice and work with the company's sector, functional, cloud, cyber, risk, Responsible AI and change specialists, as well as client owners, technology teams, operations users and alliance partners.

The role also covers supporting proposals, client workshops and market development at a level suited to your seniority, and contributing reusable methods, code and lessons learned.

Responsibilities

• Design and refine reusable platform capabilities that delivery teams use to build, deploy, secure, observe and scale AI products.
• Define reference platforms, environment layouts, network patterns, identity, secrets and access controls.
• Design model access, compute, storage, vector services, orchestration, gateways and shared platform services.
• Advise on cloud, sovereign, edge and on-premises deployment where client constraints call for them.
• Set platform non-functional requirements covering availability, performance, resilience, scalability and cost.
• Build reusable landing zones, templates and platform guardrails for delivery squads.
• Work with the MLOps Engineer on environment automation, observability and release management.
• Coach colleagues and take part in the practice's continuous learning programme.

Requirements

• 8+ years in cloud platforms, infrastructure, platform engineering or architecture.
• Deep understanding of containers, Kubernetes, networking, identity, automation, data services and AI runtimes.
• Experience with at least one major cloud platform and with hybrid integration patterns.
• Strong background in Infrastructure as Code, DevSecOps and platform governance.
• Deep experience with an enterprise AI platform such as Azure AI Foundry with Azure OpenAI, AWS Bedrock, Google Vertex AI, Databricks or an equivalent, and the ability to connect alternative model providers.
• Hands-on work with containers and Kubernetes platforms such as AKS or OpenShift, Infrastructure as Code in Terraform, Bicep or an equivalent, and GitHub Actions or Azure DevOps pipelines.
• Experience publishing and securing model, agent and tool services through API gateways, private endpoints, service identities, secrets platforms and policy enforcement, for example Microsoft Entra ID, API Management and Key Vault.
• Ability to design hybrid inference setups that combine managed endpoints with self-hosted runtimes such as vLLM, Hugging Face tooling, NVIDIA inference components, Ollama or equivalent, where suitable.
• Knowledge of vector, search and state services such as Azure AI Search, PostgreSQL with pgvector, Elasticsearch, Pinecone, Weaviate, Milvus, Redis, Cosmos DB or equivalent.
• Experience sending platform telemetry through OpenTelemetry and enterprise monitoring stacks, and designing for model and framework portability to avoid unnecessary vendor lock-in.

Conditions

Hybrid working arrangement.

Job codes: LI-BS1, LI-Hybrid.