AI Automation Engineer & Architect - Remote Full Time

Location: Remote / Global

Experience Level: Senior / Principal (10+ years)

ABOUT THE ROLE

We’re hiring an AI Automation Architect (LLM & Cloud) to architect and build the core intelligence layer behind Bold Amplify™.

This role sits at the intersection of:

• LLM systems (RAG, agents, orchestration)
• Cloud-native multi-tenant SaaS architecture
• Event-driven backend engineering
• Intelligent workflow automation
• Production reliability & observability

You will design and ship real AI systems used by real customers — not prototypes, not experiments.

If you’ve built production RAG pipelines, deployed LLM-backed services, and care about clean architecture and measurable impact — this role is for you.

WHAT YOU’LL OWN

AI Systems & RAG Architecture

• Design and deploy production-grade RAG pipelines using Vertex AI, Gemini, OpenAI, or Anthropic
• Build embedding and ingestion pipelines for structured and unstructured business data
• Implement vector search using Pinecone, Weaviate, ChromaDB, or pgvector
• Architect context management strategies balancing latency, cost, and reliability

Agentic Workflows & Orchestration

• Design multi-step agentic workflows using LangChain, LangGraph, or LlamaIndex
• Build state machines that reason, retain context, and execute business actions
• Integrate AI agents with systems like Greenhouse, HubSpot, QuickBooks, and internal services
• Implement guardrails, evaluation pipelines, and hallucination mitigation strategies

Backend & Cloud Architecture

• Build scalable backend services using Python (FastAPI preferred) and TypeScript/Node.js
• Design event-driven systems (SQS/SNS, Pub/Sub, retries, DLQs, idempotency patterns)
• Architect secure, multi-tenant SaaS infrastructure across AWS and GCP
• Manage infrastructure as code using Terraform
• Own CI/CD pipelines with GitHub Actions
• Implement observability, logging, and model monitoring

Business Translation

• Partner with product and leadership to turn operational problems into AI-driven workflows
• Define measurable success criteria for automation systems
• Communicate tradeoffs clearly (cost, token usage, reliability, latency)

WHO WE’RE LOOKING FOR

You likely match this role if you:

• Have 8+ years in software engineering
• Have 3+ years deploying AI/ML systems into production
• Have built real RAG pipelines (not tutorials)
• Have implemented LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex)
• Understand vector databases deeply
• Have strong Python and backend architecture experience
• Have worked in multi-tenant SaaS or ERP-like systems
• Have built event-driven architectures
• Have owned CI/CD and production reliability
• Think in systems and workflows — not just models

REQUIRED TECHNICAL EXPERIENCE

• Python (AI orchestration, data pipelines, backend services)
• TypeScript / Node.js (API services and integrations)
• LLM providers (Gemini preferred, OpenAI/Anthropic acceptable)
• RAG frameworks and retrieval optimization
• Vector databases (Pinecone, Weaviate, ChromaDB, pgvector)
• PostgreSQL (multi-tenant schemas, migrations)
• AWS (IAM, VPC, ECS/EKS, Lambda, SQS/SNS, RDS, Secrets)
• GCP (Vertex AI, Cloud Run, Pub/Sub)
• Terraform
• GitHub Actions CI/CD
• Event-driven architecture patterns

BONUS 

• Experience embedding AI into SaaS products
• LLM evaluation & monitoring pipelines
• Guardrails and reliability strategies
• Cost optimization for token-heavy workloads
• Experience integrating third-party SaaS tools via OAuth & webhooks
• Frontend familiarity (React/Next.js) for AI UX integration

WHAT SUCCESS LOOKS LIKE

Within 90 days you will:

• Ship at least one production-grade RAG workflow
• Deploy a multi-step agentic workflow tied to a real business function
• Establish monitoring for latency, cost, and reliability
• Contribute to scalable AI architecture standards across the platform