AI Architect

<h3>Job Summary:</h3><p>As an AI Architect you will build AI-native products. You’re a hands-on technical leader who can scope, architect, staff, and ship; then run the product safely at scale.</p><h3>Job Responsibilities:</h3><ul><li>Stand up and run squads (Discovery → Prototype → Product → Platform & SRE).</li><li>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).</li><li>Operate AI safely: evals & guardrails, structured outputs (JSON/Schema), PII redaction, refusal policies, cost/latency budgets, and LLM observability.</li><li>Own delivery outcomes: SLOs, quality, cost, velocity; release with feature flags and canaries.</li><li>Be client-facing: discovery, scoping, SoW, roadmap, QBRs.</li><li>Hire/coach Tech Leads, EMs, and PMs; level up practices.</li></ul><h3>Requirements</h3><ul><li>8–12+ yrs engineering; 4+ yrs leading multi-team delivery; shipped production web/mobile systems at scale.</li><li>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.</li><li>Implemented orchestration (LangGraph/DSPy or Temporal for durable workflows), rerankers (e.g., Cohere/Jina/Voyage), and prompt/tool versioning.</li><li>Built with modern cloud + data: serverless/K8s, Terraform, OpenTelemetry, feature flags/experimentation.</li><li>Excellent client communication and commercial sense (SoWs, staffing, utilization).</li></ul><p>Tech stack (you have hands on experience)</p><ul><li>Models: Anthropic Claude; OpenAI; Google; open-weights (Llama, Mistral).</li><li>Orchestration & agents: LangGraph (or DSPy) for graphs; Temporal for durable, long-running tasks and SLAs.</li><li>Retrieval: Postgres + pgvector (default); Weaviate/Qdrant/Pinecone when scale/ops require; hybrid search with OpenSearch/Typesense.</li><li>Embeddings / rerankers: OpenAI/Voyage/E5/BGE; Cohere/Jina/Voyage rerank.</li><li>Guardrails & evals: JSON/Pydantic schemas, red-team sets, promptfoo/Ragas/DeepEval; content/PII filters.</li><li>Observability: OpenTelemetry traces incl. prompt/tool spans; Langfuse/Arize Phoenix (or equivalent) + Sentry/Grafana.</li><li>App & data: Next.js 15 (RSC), TypeScript/Go/Python; Postgres; Kafka/Redpanda/NATS; dbt/lakehouse optional.</li><li>Ops: Cloud Run/ECS/K8s; Terraform/OpenTofu; GitHub Actions; LaunchDarkly/Unleash; Statsig/GrowthBook.</li></ul><p>Originally posted on <a href="https://himalayas.app">Himalayas</a></p>

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.

Originally posted on Himalayas