[Hiring] AI Automation Architect @Bold Business

Role Description

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.

Qualifications

• 8+ years in software engineering
• 3+ years deploying AI/ML systems into production
• Experience building real RAG pipelines (not tutorials)
• Implemented LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex)
• Strong understanding of vector databases
• Strong Python and backend architecture experience
• Experience in multi-tenant SaaS or ERP-like systems
• Experience building event-driven architectures
• Owned CI/CD and production reliability
• Think in systems and workflows — not just models

Requirements

• 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

Benefits

• 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