We are looking for one architect to own both layers. Your foundation is deep platform and DevOps architecture: reliability, scalability, security, and cost efficiency of the cloud platform every R&D team builds on. Your growth edge is the AI layer: designing how agents are orchestrated, how they access tools and data safely, how we evaluate whether they work, and how we govern their cost and behavior in production.
We know agentic AI is a young discipline. We are not looking for a decade of AI experience that does not exist. We are looking for a proven platform architect who has spent the last one to two years genuinely building LLM-based and agentic systems in production, and who wants to own where this field goes inside a company that takes it seriously.
What Youll Do:
Platform & DevOps
Cloud Architecture: Own the architecture of our cloud platform, including GKE fleet design, VPC networking, IAM/Workload Identity, and multi-environment strategies across GCP (and AWS where relevant).
IaC & GitOps: Lead Infrastructure-as-Code (Pulumi, Terraform) standards, ArgoCD deployment patterns, and secure CI/CD paved paths (GitHub Actions, GitLab CI) adopted by all R&D teams.
Reliability & Observability: Own reliability architecture, including Disaster Recovery (DR) strategy and drills, P1 incident reduction, MTTR improvement, and observability platform architecture (Datadog, Prometheus, Grafana, OpenTelemetry) including usage and cost optimization.
FinOps: Drive FinOps as an architectural discipline-rightsizing, idle-resource elimination, unallocated-spend attribution, and cost-aware design reviews using tools like Kubecost or GCP Cost Management.
Agentic AI
Agent Architecture: Design our agentic AI ecosystem, focusing on agent orchestration frameworks (e.g., LangChain, LangGraph, CrewAI, AutoGen), tool/MCP (Model Context Protocol) interfaces, vector databases/memory strategies (e.g., Pinecone, Qdrant, pgvector), and production AI deployment patterns.
Evaluation & Guardrails: Build the evaluation and guardrail layer for AI in production using frameworks like Ragas, TruLens, or LangSmith. Define permission models, human-in-the-loop checkpoints, audit logging, and policy-as-code for AI usage.
AI FinOps & Monitoring: Own AI cost observability, token spend monitoring, model routing strategies (optimizing for cost/quality/latency trade-offs), and real-time alerting on runaway model usage.
Reference Architectures & Standards: Define reference architectures for teams building AI features, RAG patterns, prompt versioning/management, and model API standards (OpenAI API, Anthropic, open-source models via vLLM/Ollama), driving high-leverage AI automation across R&D.
Requirements: Experience: 8+ years in platform/infrastructure engineering, DevOps, or systems architecture, with proven ownership of enterprise-scale platform decisions.
Containers & Orchestration: Deep production expertise with Kubernetes (GKE strongly preferred), container runtime, and Service Mesh technologies.
IaC & GitOps: Hands-on mastery of Infrastructure as Code using Terraform or Pulumi, along with GitOps practices via ArgoCD.
Cloud Platform Depth: Strong GCP architecture depth-networking, IAM, Workload Identity, and cloud cost management.
Production LLM / Agentic Systems: 1-2+ years of hands-on experience building LLM-based or agentic systems that have reached production-agent frameworks, tool/function calling, and shipped Retrieval-Augmented Generation (RAG) systems (not just tutorials or prototypes).
Reliability Track Record: A proven track record of measurable reliability and cost outcomes (e.g., uptime improvements, MTTR reduction, and cloud spend optimization).
Streaming Data: Solid experience with Apache Kafka or comparable streaming platforms at scale.
Soft Skills: Ability to lead through influence, mentor engineering teams, and communicate complex architectural trade-offs clearly to both engineers and executives.
This position is open to all candidates.