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
- 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