AI Full Stack Engineer

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

• Production 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.)

Benefits

• Competitive salary based on experience

• High-impact role with strong ownership

• Opportunity to scale cutting-edge AI systems to world-class level

Originally posted on Himalayas