Full Stack Automation Engineer

WHO WE ARE

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

OUR CORE VALUES

Be A Moral Human

Serve a higher purpose. Everything we build and every decision we make is grounded in doing the right thing.

Improve 1% Daily

Strive for 1% better every day. Consistent, compounding improvement is how we build world-class systems and teams.

Extreme Ownership

Own your actions. No excuses, no hand-offs as a crutch. If it's in your world, it's your responsibility.

Solve Problems, Don't Create Them

Every challenge has a solution. Don't slow down the team by creating new problems — come with answers, not blockers.

Make an Impact

Focus on meaningful results. We measure success by the difference we make, not the hours we log.

Fail Fast & Fail Forward

Don't be afraid to fail. Take that failure, learn from it, and move forward stronger. Every failure is a lesson.

WHAT YOU'LL BUILD & SCALE

AI Communication Pipelines

• Classify inbound messages by category, intent, urgency, and tone
• Generate contextual responses using enrichment data
• Implement and tune human approval gates

AI-Powered Sales Intelligence

• Transform raw enrichment data into structured pre-call briefs
• Generate backgrounds, pain hypotheses, talking points, and rapport hooks

RAG System

• Maintain and improve the vector database with embeddings
• Implement markdown-aware chunking strategies
• Build async ingestion workers and semantic search APIs

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

• Extend the multi-agent system (outline → audit → generate)
• Maintain binary quality gates (PASS/FAIL with citations)
• Support multiple content formats across the pipeline

Automated Lead Qualification

• Enrich leads with product data and market insights
• Build AI scoring and qualification grading systems
• Generate automated audit reports

AI Executive Assistant

• Build and maintain Slack-integrated operations
• Automate scheduling workflows
• Triage and respond to email autonomously

Requirements

DAY-TO-DAY RESPONSIBILITIES

• Build and improve AI pipelines for client performance insights
• Improve RAG retrieval quality (re-ranking, chunking, hybrid search)
• Add tool use / function calling for real-time data in LLM pipelines
• Debug classification errors and improve model accuracy
• Optimize LLM costs, latency, and performance
• Build dashboards for AI metrics and usage monitoring
• Add observability and tracing to AI pipelines
• Expand content quality systems to new formats and use cases

TECH STACK

Core: TypeScript

• Node.js
• React / Next.js
• n8n
• PostgreSQL
• CI/CD
• Claude Code

Nice to have: AWS Lambda

• Terraform
• Docker
• Amazon SP-API
• Slack Bots
• Playwright

QUALIFICATIONS

Required:

• Production LLM experience — Claude or OpenAI deployed in real, live systems
• RAG system experience — embeddings, retrieval, chunking, and context handling
• 3+ years TypeScript / Node.js
• Strong React skills (component architecture, state management, performance)
• PostgreSQL — queries, migrations, indexing, query optimisation
• API integrations — REST, OAuth, webhooks
• Linux server experience — SSH, log analysis, debugging, deployments

Strong Pluses:

• Multi-agent