Full Stack Automation Engineer
- Role
- Fullstack
- Experience
- Mid
- Employment
- Full-time
Open to BR, CO only. Set where you work from to check your eligibility.
No BS summary
Full stack engineer with production LLM/RAG experience, 3+ years TypeScript/Node.js, strong React, PostgreSQL, Linux, AWS Lambda, Terraform, and Docker. Must be based in Brazil or Colombia and available during Eastern Time business hours.
Core skills
Required skills
Optional skills
We're hiring a 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 3+ 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 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
What you'll do
- Build, optimize, and scale AI-powered infrastructure across the full stack — from LLM pipelines and RAG systems to dashboards and background workers.
- Classify inbound messages by category, intent, urgency, and tone.
- Generate contextual responses using enrichment data.
- Implement and tune human approval gates.
- Transform raw enrichment data into structured pre-call briefs.
- Generate backgrounds, pain hypotheses, talking points, and rapport hooks.
- Maintain and improve the vector database with embeddings.
- Implement markdown-aware chunking strategies.
- Build async ingestion workers and semantic search APIs.
- Process RSS feeds, social media, video platforms, and search trends.
- Generate reports, forecasts, and content drafts.
- Run autonomously on scheduled jobs.
- Extend the multi-agent system (outline → audit → generate).
- Maintain binary quality gates (PASS/FAIL with citations).
- Support multiple content formats across the pipeline.
- Enrich leads with product data and market insights.
- Build AI scoring and qualification grading systems.
- Generate automated audit reports.
- 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.
What they require
- Production LLM experience — Claude or OpenAI deployed in real, live systems.
- RAG system experience — embeddings, retrieval, chunking, and context handling.
- 3+ 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.
- Preferred: Multi-agent LLM systems and orchestration.
- Preferred: Anthropic Claude expertise (prompt engineering, tool use, system prompts).
- Preferred: Vector search and embeddings (pgvector, Pinecone, or similar).
- Preferred: Slack API and bot development.
- Preferred: Ad platform APIs (Meta, Google, LinkedIn).
- Preferred: LLM observability — cost tracking, tracing, monitoring.
- Preferred: AI-assisted dev tools (Cursor, Claude Code, etc.).
Benefits
- High-impact role with genuine ownership over systems that matter.
- 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.