AI Full Stack Engineer - KS001

GigaBrands • Austin, TX

6h

About The Position

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. The platform is already live and scaling fast, with numerous background services, frontend pages, backend services, database tables, and 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.

Requirements

• 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)

Nice To Haves

• 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.)

Responsibilities

• Classify inbound messages by category, intent, urgency, and tone
• Generate contextual responses using enrichment data
• Implement human approval gates
• Transform raw enrichment data into structured pre-call briefs
• Generate background, pain hypotheses, talking points, rapport hooks
• Build RAG system with vector database with embeddings, Markdown-aware chunking, async ingestion workers, and semantic search API
• Process RSS feeds, social media, video platforms, and search trends
• Generate reports, forecasts, and content drafts
• Run autonomously on scheduled jobs
• Build multi-agent content quality systems (outline → audit → generate) with binary quality gates (PASS/FAIL with citations)
• Support multiple content formats
• Enrich leads with product data and market insights
• AI scoring and qualification grading
• Build automated audit reports
• Build AI Executive Assistant features including Slack operations, scheduling workflows, email triage and follow-ups
• 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

Benefits

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

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed