AI Full Stack Engineer - KS001 We’ve built an AI-native internal platform that powers every aspect of our Amazon brand management business. AI isn’t a feature — it’s the backbone. LLMs classify and respond to inbound communications AI generates pre-call intelligence briefs from raw enrichment data A RAG system feeds context into every generation pipeline An AI checkpoint system audits all generated content against quality gates The platform is already live and scaling fast: 17+ background services 130+ frontend pages 214 backend services 184 database tables Dozens of autonomous AI pipelines We’re hiring an engineer who operates at the intersection of AI and production systems. You’ll build, optimize, and scale AI-powered infrastructure across the full stack.What You’ll Build & ScaleAI Communication Pipelines Classify inbound messages by category, intent, urgency, and tone Generate contextual responses using enrichment data Implement human approval gates AI-Powered Sales Intelligence Transform raw enrichment data into structured pre-call briefs Generate: background, pain hypotheses, talking points, rapport hooks RAG System Vector database with embeddings Markdown-aware chunking Async ingestion workers Semantic search API Trend Intelligence Engine Process RSS feeds, social media, video platforms, and search trends Generate reports, forecasts, and content drafts Run autonomously on scheduled jobs Content Quality Pipeline Multi-agent system (outline → audit → generate) Binary quality gates (PASS/FAIL with citations) Supports multiple content formats Automated Lead Qualification Enrich leads with product data and market insights AI scoring and qualification grading Automated audit reports AI Executive Assistant Slack operations Scheduling workflows Email triage and follow-ups Build AI pipelines for client performance insights Improve RAG retrieval quality Add tool use for real-time data in LLM pipelines Debug classification errors in AI systems Optimize LLM costs and performance Build dashboards for AI metrics and usage Add observability to pipelines Expand content quality systems QualificationsProduction LLM experience (Claude/OpenAI in real systems) RAG system experience (embeddings, retrieval, chunking, context handling) 3+ years TypeScript / Node.js Strong React skills PostgreSQL (queries, migrations, indexing) API integrations (REST, OAuth, webhooks) Linux server experience (SSH, logs, debugging, deployments) Strong Pluses Multi-agent LLM systems Anthropic Claude expertise Vector search / embeddings Slack API experience Ad platform APIs (Meta, Google, LinkedIn) LLM observability (cost, tracing, monitoring) Amazon / eCommerce experience AI-assisted dev tools (Cursor, Claude Code, etc.) Competitive salary based on experience High-impact role with strong ownership Opportunity to scale cutting-edge AI systems to world-class level We’ve built an AI-native internal platform that powers every aspect of our Amazon brand management business. AI isn’t a feature — it’s the backbone. LLMs classify and respond to inbound communications AI generates pre-call intelligence briefs from raw enrichment data A RAG system feeds context into every generation pipeline An AI checkpoint system audits all generated content against quality gates The platform is already live and scaling fast: 17+ background services 130+ frontend pages 214 backend services 184 database tables Dozens of autonomous AI pipelines We’re hiring an engineer who operates at the intersection of AI and production systems. You’ll build, optimize, and scale AI-powered infrastructure across the full stack.What You’ll Build & ScaleAI Communication Pipelines Classify inbound messages by category, intent, urgency, and tone Generate contextual responses using enrichment data Implement human approval gates AI-Powered Sales Intelligence Transform raw enrichment data into structured pre-call briefs Generate: background, pain hypotheses, talking points, rapport hooks RAG System Vector database with embeddings Markdown-aware chunking Async ingestion workers Semantic search API Trend Intelligence Engine Process RSS feeds, social media, video platforms, and search trends Generate reports, forecasts, and content drafts Run autonomously on scheduled jobs Content Quality Pipeline Multi-agent system (outline → audit → generate) Binary quality gates (PASS/FAIL with citations) Supports multiple content formats Automated Lead Qualification Enrich leads with product data and market insights AI scoring and qualification grading Automated audit reports AI Executive Assistant Slack operations Scheduling workflows Email triage and follow-ups Build AI pipelines for client performance insights Improve RAG retrieval quality Add tool use for real-time data in LLM pipelines Debug classification errors in AI systems Optimize LLM costs and performance Build dashboards for AI metrics and usage Add observability to pipelines Expand content quality systems QualificationsProduction LLM experience (Claude/OpenAI in real systems) RAG system experience (embeddings, retrieval, chunking, context handling) 3+ years TypeScript / Node.js Strong React skills PostgreSQL (queries, migrations, indexing) API integrations (REST, OAuth, webhooks) Linux server experience (SSH, logs, debugging, deployments) Strong Pluses Multi-agent LLM systems Anthropic Claude expertise Vector search / embeddings Slack API experience Ad platform APIs (Meta, Google, LinkedIn) LLM observability (cost, tracing, monitoring) Amazon / eCommerce experience AI-assisted dev tools (Cursor, Claude Code, etc.)