Automation AI Engineer

GigaBrands; United States; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Job summary

GigaBrands is seeking an AI Full Stack Engineer to build, optimize, and scale AI-powered infrastructure across the full stack for our Amazon brand management platform. You will work on LLM-driven classification, generation, and governance, integrating vector search, embeddings, and a robust data pipeline stack.

You will implement end-to-end AI solutions, from data enrichment and pre-call intelligence to automated content generation and dashboards, with emphasis on observability and cost

Qualifications

• Production LLM experience in real systems.
• Experience with RAG systems including embeddings, chunking and context handling.
• 3+ years TypeScript/Node.js in production.
• Strong React skills and front-end excellence.
• Experience with PostgreSQL (queries, migrations, indexing) and API integrations (REST, OAuth, webhooks).

Responsibilities

• Build AI pipelines for client performance insights and scale AI-powered infrastructure.
• Improve RAG retrieval quality and add real-time data tooling in LLM pipelines.
• Debug classification errors and optimize LLM costs and performance.
• Build dashboards for AI metrics and usage and add observability to pipelines.

Skills

Production LLM

RAG system

TypeScript/Node.js

React

API integrations

Embeddings

Vector search

Linux

Tools

PostgreSQL

REST

OAuth

Webhooks

Slack API

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