AI Full Stack Engineer

Sobre a oportunidade

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