Principal Architect (ai, Cloud & Azure)

MetLife MetLife · Insurance

Principal Architect role focused on defining and governing enterprise architectures for AI, Generative AI, and Agentic AI platforms, including model lifecycle management, orchestration, tooling, and integration. The role involves architecting LLM-based solutions using RAG, fine-tuning, prompt engineering, and agent patterns, and establishing standards for ML/GenAI tooling and MLOps/LLMOps pipelines. It also requires ensuring responsible AI principles and compliance, with a strong emphasis on Azure cloud architecture and infrastructure reliability.

What you'd actually do

  1. Define and evolve current‑state and target‑state architectures, reference architectures, standards, principles, and roadmaps across AI, GenAI, Agentic AI, Cloud, Infrastructure, IAM, and CIAM domains.
  2. Provide architectural leadership for Microsoft Azure including landing zones, identity integration, governance, networking, security, resilience, and cost controls.
  3. Define enterprise architecture for AI, GenAI, and Agentic AI platforms including model lifecycle management, orchestration, tooling, and integration with enterprise platforms.
  4. Architect Large Language Model (LLM) based solutions using commercial and open‑source models, applying retrieval‑augmented generation (RAG), fine‑tuning, prompt engineering, and agent‑based patterns.
  5. Establish standards and reference architectures for ML and GenAI tooling such as feature stores, vector databases, model registries, MLOps / LLMOps pipelines, and AI observability frameworks.

Skills

Required

  • Deep expertise in Microsoft Azure architecture and cloud platforms
  • Strong hands‑on knowledge of AI, GenAI, Agentic AI, LLMs, and ML platforms
  • Experience with ML tools, frameworks, and AI orchestration patterns
  • IAM and CIAM security‑by‑design principles
  • Executive communication, influence, and decision‑making

Nice to have

  • Microsoft Certified: Azure Solutions Architect Expert
  • Microsoft Azure AI Engineer Associate or Azure AI certification
  • Architecture, cloud, security, or identity certifications

What the JD emphasized

  • AI governance forums
  • regulatory compliance
  • AI risk standards

Other signals

  • Define enterprise architecture for AI, GenAI, and Agentic AI platforms including model lifecycle management, orchestration, tooling, and integration with enterprise platforms.
  • Architect Large Language Model (LLM) based solutions using commercial and open-source models, applying retrieval-augmented generation (RAG), fine-tuning, prompt engineering, and agent-based patterns.
  • Establish standards and reference architectures for ML and GenAI tooling such as feature stores, vector databases, model registries, MLOps / LLMOps pipelines, and AI observability frameworks.