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Junior Full Stack Automation Engineer

We're hiring a Junior Full Stack Automation Engineer who operates at the intersection of AI and production systems. You'll build, optimize, and scale AI-powered infrastructure across the full stack — from LLM pipelines and RAG systems to dashboards and background workers. This is a high-ownership role. You won't be handed tickets. You'll be handed problems and trusted to solve them.

WHAT YOU'LL BUILD & SCALE

AI Communication Pipelines

  • Classify inbound messages by category, intent, urgency, and tone
  • Generate contextual responses using enrichment data
  • Implement and tune human approval gates

AI-Powered Sales Intelligence

  • Transform raw enrichment data into structured pre-call briefs
  • Generate backgrounds, pain hypotheses, talking points, and rapport hooks

RAG System

  • Maintain and improve the vector database with embeddings
  • Implement markdown-aware chunking strategies
  • Build async ingestion workers and semantic search APIs

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

  • Extend the multi-agent system (outline → audit → generate)
  • Maintain binary quality gates (PASS/FAIL with citations)
  • Support multiple content formats across the pipeline

Automated Lead Qualification

  • Enrich leads with product data and market insights
  • Build AI scoring and qualification grading systems
  • Generate automated audit reports

AI Executive Assistant

  • Build and maintain Slack-integrated operations
  • Automate scheduling workflows
  • Triage and respond to email autonomously
  • Build and improve AI pipelines for client performance insights
  • Improve RAG retrieval quality (re-ranking, chunking, hybrid search)
  • Add tool use / function calling for real-time data in LLM pipelines
  • Debug classification errors and improve model accuracy
  • Optimize LLM costs, latency, and performance
  • Build dashboards for AI metrics and usage monitoring
  • Add observability and tracing to AI pipelines
  • Expand content quality systems to new formats and use cases

Requirements

Required:

  • Production LLM experience — Claude or OpenAI deployed in real, live systems
  • RAG system experience — embeddings, retrieval, chunking, and context handling
  • 2+ years TypeScript / Node.js
  • 2-3 years building end-to-end production systems spanning backend services, AI pipelines, and frontend dashboards
  • Bachelor's degree in Computer Science
  • Strong React skills (component architecture, state management, performance)
  • PostgreSQL — queries, migrations, indexing, query optimisation
  • API integrations — REST, OAuth, webhooks
  • Linux server experience — SSH, log analysis, debugging, deployments
  • AWS Lambda, Terraform, and Docker experience
  • Available during Eastern Time business hours

Strong Pluses:

  • Multi-agent LLM systems and orchestration
  • Anthropic Claude expertise (prompt engineering, tool use, system prompts)
  • Vector search and embeddings (pgvector, Pinecone, or similar)
  • Slack API and bot development
  • Ad platform APIs (Meta, Google, LinkedIn)
  • LLM observability — cost tracking, tracing, monitoring
  • AI-assisted dev tools (Cursor, Claude Code, etc.)

Benefits

WHAT WE OFFER

  • High-impact role with genuine ownership over systems that matter
  • Salary from $2,500 - $3,500
  • Full time remote role
  • Work directly on one of the most advanced AI-native business platforms in the Amazon space
  • A team that moves fast, thinks big, and holds a high bar
  • PTO after successfully completed probationary period
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What this application asks

workable

First name, Last name, Email, Headline, Phone, Address, Photo, What is your desired monthly salary in American Dollars (USD)?, Education, Experience, Summary, Resume, Are you available to work from 9:00 AM to 6:00 PM EST?, Are you available to work full-time (40 hours per week)?, When could you start if hired?*

  • Could you share your experience deploying Claude or OpenAI models in production environments? What kinds of systems did you build, and what challenges did you overcome? written answer
  • Can you describe your experience building Retrieval-Augmented Generation (RAG) systems? What approaches did you use for embeddings, retrieval, chunking, and context management? written answer
  • How many years have you worked with TypeScript and Node.js, and what types of projects have you used them for? written answer
  • Could you tell us about your experience building end-to-end production systems that include backend services, AI pipelines, and frontend dashboards? What were some of the key outcomes or features you delivered? written answer

See also

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