[Hiring] Senior AI Engineer @Go Cadre

Role Description

You are the technical backbone of Cadre AI's delivery engine. While AI strategists own the client relationship and translate business problems into requirements, you own the build:

• Complex backend AI modules
• Agentic workflows
• Retrieval systems over messy unstructured data
• The infrastructure that keeps them running at scale

There are no over-the-wall handoffs here. You co-develop requirements in real time alongside the strategist, which means what gets built is precise, purposeful, and shipped fast.

You thrive on deep, uninterrupted technical execution. You are not a generalist who dabbles in AI. You have built production LLM systems, tuned RAG pipelines until retrieval accuracy became a non-issue, and debugged agentic chains under pressure. You know the difference between a prototype and a production-grade system, and you build the latter by default.

Beyond client delivery, you look for patterns. When three clients ask for similar components, you abstract the best version into an internal tool that speeds up every future delivery. That force-multiplier instinct is what separates this role from a traditional contractor engagement.

Qualifications

• 3+ years of backend engineering experience
• At least 1 year building and shipping production AI agents in a client-facing or fast-paced product environment
• Deep, hands-on experience with vector databases, advanced RAG optimization, and agentic orchestration
• Proficiency in Python and Node.js
• Strong command of backend architecture patterns: API design, async processing, parallel execution, and secure credential handling
• Infrastructure fluency: use of IaC tools (Terraform or Pulumi), Docker, and Kubernetes
• Systems design instincts that favor abstraction and reuse
• Certified Claude Architect or the ability to earn the certification within your first 60 days
• AI-native working style
• Complexity tolerance
• Ownership orientation

Requirements

• Architect and optimize RAG pipelines over unstructured, messy, and multi-modal data sources
• Implement vector database solutions based on query patterns, data volume, and latency requirements
• Diagnose and fix retrieval failures in production
• Build and maintain agentic systems with attention to tool selection, memory architecture, and failure recovery
• Manage context window constraints deliberately
• Optimize prompt pipelines for latency and cost
• Own deployment of AI services using modern IaC tooling and containerized environments
• Write tests at the right level of the pyramid
• Maintain production systems with structured logging, alerting, and cost monitoring
• Identify patterns across client engagements and abstract the best implementations into reusable internal tools
• Document internal tools clearly
• Actively contribute to Cadre's internal AI platform

Benefits

• Real ownership: run engagements end-to-end without handoffs
• Variety and velocity: every engagement is a different industry and problem
• AI-native culture: use AI to run operations
• Access to the frontier: experiment with new models and capabilities
• Upside: share in the success of a bootstrapped, profitable, and fast-growing company
• No bureaucracy: small pods with clear accountability