Talent Voyager
Tech / AI / Software
AIFullStackEngineer
Neural analysis suggests this role is
optimal for Mid candidates.
“AI Full Stack Engineer at Talent Voyager. Skills: AI Full Stack Engineering, LLM, RAG system, TypeScript, Node.js, React, PostgreSQL. Build AI pipelines for client performance insights. Improve RAG retrieval quality”
What You'll Achieve.
Scale AI-powered infrastructure; Scale cutting-edge AI systems to world-class level
Industry & Context.
Debug classification errors in AI systems
What They're Looking For.
Must Have
3+ years TypeScript / Node. js, React skills, PostgreSQL (queries, migrations, indexing), API integrations (REST, OAuth, webhooks), Linux server experience (SSH, logs, debugging, deployments)
Nice to Have
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.)
What You'll Do.
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
Classify inbound messages by category
Generate contextual responses using enrichment data
Implement human approval gates
Transform raw enrichment data into structured pre-call briefs
Run autonomously on scheduled jobs
Audit generated content against quality gates
Enrich leads with product data and market insights
AI scoring and qualification grading
Automated audit reports
Email triage and follow-ups
Full Job Description
We are hiring an **AI Full Stack Engineer** on behalf of our Client, a fast-growing marketing agency based in the United States, specializing in the Amazon ecosystem. They’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 wo
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