Job Description

AI Full Stack Engineer

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 & Scale

AI 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

Requirements

Key Responsibilities

• 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

Qualifications

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.)

Benefits

• Competitive salary based on experience
• High-impact role with strong ownership
• Opportunity to scale cutting-edge AI systems to world-class level