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.
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.
