AI Platform Architect

About the role

The AI Platform Architect will design and evolve reusable platform capabilities to build, deploy, secure, observe and scale AI products across hybrid environments. This role focuses on defining reference platforms, ensuring reliability, compliance, and supporting diverse deployment models.

Key responsibilities

• Define reference platforms, environment topology, network patterns, identity, secrets and access controls.
• Design model access, compute, storage, vector services, orchestration, gateways and shared platform services.
• Support cloud, sovereign, edge and on‑premises deployment choices based on client constraints.
• Define non‑functional requirements for availability, performance, resilience, scalability and cost.
• Create reusable landing patterns, templates and platform guardrails for delivery squads.
• Partner with MLOps Engineer on environment automation, observability and release management.

Required profile

• 8+ years of experience 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 hybrid integration patterns.
• Strong Infrastructure as Code, DevSecOps and platform‑governance orientation.

Required skills

• Enterprise AI platforms such as Azure AI Foundry, Azure OpenAI, AWS Bedrock, Google Vertex AI, Databricks.
• Containers and Kubernetes platforms (AKS, OpenShift).
• Infrastructure as Code tools (Terraform, Bicep).
• CI/CD pipelines (GitHub Actions, Azure DevOps).
• API management and security tools (Microsoft Entra ID, API Management, Key Vault).
• Hybrid inference technologies (vLLM, Hugging Face, NVIDIA inference components, Ollama).
• Vector and search services (Azure AI Search, PostgreSQL + pgvector, Elasticsearch, Pinecone, Weaviate, Milvus, Redis, Cosmos DB).
• Telemetry integration with OpenTelemetry.