AI Platform Architect

Job-:
Information Technology

Location:

Orlando, FL, United States Workplace:
Hybrid

Schedule:

Full time Posted:
At Signature Aviation we are modernizing operations and customer experience through advanced data platforms and artificial intelligence. We are hiring an AI Platform Architect to design, build, and operate the Azure foundation that AI initiatives across the enterprise run on: the AI landing zone, the Azure API Management gateway that fronts all model access, the model evaluation framework, AI Gateway and the governed Azure sandboxes where teams experiment.

You will define the standards rather than inherit them, and you will prove them by shipping real use cases on the platform you build.

Working model — forward deployed: you embed with the business unit or engineering team that owns a use case, build inside the environment and manage the release process, hand back a running system, and generalize what you built into platform capability between deployments. Qualifications

Minimum Education and/or Experience
10+ years in software, cloud, or platform engineering, spent primarily hands-on.

You have built an AI or ML platform that other teams used in production — you owned it, or a major component of it, including workloads running in live operational settings.

Kubernetes and distributed systems depth, with strong cloud networking, security architecture, and reliability engineering practice.

Azure is required:
Microsoft Foundry (formerly Azure AI Foundry) including Azure OpenAI models — projects and deployments, the Foundry Models catalog, the Foundry Agent Service, the Microsoft Agent Framework, and Azure AI Content Safety.

AKS or Azure Container Apps, Bicep or Terraform, and Azure Dev Ops or Git Hub Actions — pipelines and modules you wrote, not ones you inherited.

You have built or operated an LLM/AI gateway: token metering, model routing, quota enforcement, caching. Azure API Management preferred;
LiteLLM, Kong, Apigee, or a bespoke proxy considered on merit.

Vector and hybrid retrieval — Azure AI Search, Cosmos DB, or pgvector — plus embedding and ingestion pipelines that stayed correct as source data changed.

You have built model evaluation from scratch — datasets, metrics, judges, regression suites — and can explain how you knew a model change was an improvement.

Microsoft Entra , Azure RBAC, Key Vault, and Private Link in a production estate.

Strong Python, plus enough Type Script to build and debug an application front to back.

You have delivered while embedded with a business or engineering team, accountable for the outcome rather than for a component handed across a boundary.

Enterprise judgment on data classification, PII handling, access control, auditability, and responsible AI.Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.

Preferred Qualifications Strong Lang Graph, multi-agent or agent-runtime experience. MCP, enterprise tool gateways and identity-aware tool execution. Agent memory and context management architectures. Azure API Management, LiteLLM, Kong, Portkey or similar AI gateway/model proxy technologies.

Microsoft Foundry, Azure OpenAI, Azure AI Search, Azure Databricks or Microsoft Fabric.

Open Telemetry, Langfuse, Lang Smith, Ragas, Promptfoo or Azure AI Evaluation SDK.Temporal, Azure Durable Functions or similar durable workflow technologies.

MLflow and model lifecycle management.

Commercial and open-source model deployment, including GPU workloads.

Experience in a Forward Deployed Engineer, platform consulting, solutions engineering, startup or internal innovation environment.

Aviation, logistics, mobility or other large-scale operational environments.

Additional Information This is an applied engineering…