AI Architect
- Seniority
- Staff Principal
- Work model
- Hybrid
- Posted
- 5d ago
typescriptpythongonext.jspostgreskafkakubernetesterraformserverlessdbtllmgithub actions
Job Summary: As an AI Architect you will build AI-native products. You’ll lead cross-functional Innovation Delivery Squads—owning outcomes end-to-end across web, mobile, AI agents, and streaming backends. You’re a hands-on technical leader who can scope, architect, staff, and ship; then run the product safely at scale. Job Responsibilities: Stand up and run squads (Discovery → Prototype → Product → Platform & SRE). Design and ship RAG/agent systems: pick models (e.g., Anthropic Claude, OpenAI, Google, or open-weights like Llama/Mistral), define tools/functions, and choose retrieval (default Postgres + pgvector, scale to Weaviate/Qdrant/Pinecone when needed). Operate AI safely: evals & guardrails, structured outputs (JSON/Schema), PII redaction, refusal policies, cost/latency budgets, and LLM observability. Own delivery outcomes: SLOs, quality, cost, velocity; release with feature flags and canaries. Be client-facing: discovery, scoping, SoW, roadmap, QBRs. Hire/coach Tech Leads, EMs, and PMs; level up practices. Requirements 8–12+ yrs engineering; 4+ yrs leading multi-team delivery; shipped production web/mobile systems at scale. Shipped at least one production AI app using Claude/GPT/Gemini/Llama/Mistral, backed by retrieval (pgvector or a vector DB) and a basic eval/guardrail pipeline. Implemented orchestration (LangGraph/DSPy or Temporal for durable workflows), rerankers (e.g., Cohere/Jina/Voyage), and prompt/tool versioning. Built with modern cloud + data: serverless/K8s, Terraform, OpenTelemetry, feature flags/experimentation. Excellent client communication and commercial sense (SoWs, staffing, utilization). Tech stack (you have hands on experience) Models: Anthropic Claude; OpenAI; Google; open-weights (Llama, Mistral). Orchestration & agents: LangGraph (or DSPy) for graphs; Temporal for durable, long-running tasks and SLAs. Retrieval: Postgres + pgvector (default); Weaviate/Qdrant/Pinecone when scale/ops require; hybrid search with OpenSearch/Typesense. Embeddings / rerankers: OpenAI/Voyage/E5/BGE; Cohere/Jina/Voyage rerank. Guardrails & evals: JSON/Pydantic schemas, red-team sets, promptfoo/Ragas/DeepEval; content/PII filters. Observability: OpenTelemetry traces incl. prompt/tool spans; Langfuse/Arize Phoenix (or equivalent) + Sentry/Grafana. App & data: Next.js 15 (RSC), TypeScript/Go/Python; Postgres; Kafka/Redpanda/NATS; dbt/lakehouse optional. Ops: Cloud Run/ECS/K8s; Terraform/OpenTofu; GitHub Actions; LaunchDarkly/Unleash; Statsig/GrowthBook.