Automation AI Engineer at Gigabrands – Mexico | Jobyssey

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

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