Generative AI Systems Architect

About the role

We are looking for a Generative AI Systems Architect to lead the design and delivery of an enterprise‑wide Generative AI platform. The role will define reference architectures, guardrails and roadmaps for scalable, secure LLM‑based solutions across multiple clouds, working closely with engineering, security, data governance and product teams.

Key responsibilities

• Design enterprise generative AI reference architectures, blueprints and reusable patterns.
• Define multi‑cloud foundations covering networking, identity and secrets management.
• Lead LLMOps/ModelOps initiatives, including model evaluation, safety, observability and rollout strategies.
• Create governance frameworks for agentic systems and the Model Context Protocol (MCP).
• Ensure compliance with security, risk, Responsible AI and PII controls.
• Drive cost‑efficiency, reliability and performance through capacity planning and FinOps.
• Improve developer experience with CI/CD pipelines, golden paths, templates and Infrastructure as Code.
• Mentor engineers and advocate best practices across the organization.

Required profile

• Bachelor’s degree in Computer Science, Engineering or related field, or equivalent experience.
• Proven experience architecting cloud‑based production Generative AI/LLM systems.
• Strong background collaborating with cross‑functional teams to translate business goals into platform designs.

Required skills

• Cloud platforms: Azure, AWS, GCP.
• Infrastructure as Code: Terraform, Bicep, CDK, CloudFormation.
• Containerization & orchestration: Docker, Kubernetes.
• API management: API gateways, service meshes.
• CI/CD & release management: Jenkins, GitHub Actions, GitLab CI, Azure DevOps.
• LLM inference frameworks: vLLM, Triton, TGI, Ray Serve, KServe/Seldon.
• Observability & tracing: OpenTelemetry, Langfuse, Arize Phoenix, WhyLabs.
• Evaluation pipelines and regression suites.
• Embedding generation and vector databases: Pinecone, Weaviate, Milvus, FAISS.
• Agentic system design: LangGraph, CrewAI, AutoGen‑like workflows, Model Context Protocol.
• Security and compliance guardrails: secrets isolation, prompt‑injection defenses, tool/API permissions.