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AI Architect
Posted by Creative Chaos on 23 February 2026, 252 days ago. Still on their Workable board when we checked 34 min ago. Half of Creative Chaos's roles come down within 294 days.
What the posting is about
Lead cross-functional teams to build AI-native products. Design and ship RAG/agent systems using various models and retrieval methods. Ensure safe AI operation with evals, guardrails, and observability. Own delivery outcomes and be client-facing. Requires 8-12+ years of engineering experience with 4+ years leading multi-team delivery.
Read out of the posting
Levelmid
Experience asked8+ years
EmploymentFull time
LocationPakistan
RemoteYes
Visa sponsorshipNot stated
SalaryNot published, and most postings do not
Posted2026-02-23
Found viaworkable, direct from their system
We saw it 7 months after it went up.
The posting, as the company wrote it
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.
Copied from Creative Chaos’s own board, not rewritten.
Also open at Creative Chaos
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