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 - 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. 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 - Infrastructure fluency with IaC tools (Terraform or Pulumi), Docker, and Kubernetes - Certified Claude Architect or ability to earn the certification within your first 60 days - AI-native working style - Complexity tolerance - Ownership orientation Requirements - Design and build complex backend AI modules - Translate architectural blueprints into high-performance, production-ready code - Own end-to-end delivery of AI components - Architect and optimize RAG pipelines - Implement vector database solutions - Diagnose and fix retrieval failures in production - Build and maintain agentic systems - Manage context window constraints - Optimize prompt pipelines for latency and cost - Own deployment of AI services using modern IaC tooling - Write tests at the right level of the pyramid - Maintain production systems with structured logging and alerting - Identify patterns across client engagements - Document internal tools clearly - Contribute to Cadre's internal AI platform Benefits - Real ownership - AI-native culture - Access to the frontier through partnerships with Anthropic, OpenAI, and YC - Upside with bootstrapped, profitable growth - No bureaucracy with small pods and clear accountability Compensation The base pay range for this role is $5,000 USD to $7,000 USD. If you have built production AI systems that other engineers point to as examples of how it should be done, and you want to do that work across a portfolio of complex, high-stakes client problems, this is the role.

• Define precise technical requirements for scalable agent architecture, including necessary reasoning frameworks, risk management systems, and knowledge retrieval interfaces required for complex decision-making. • Serve as the hands-on technical lead capable of building, testing, and deploying both interim (e.g., RAG-based systems) and highly complex agentic AI solutions. • Work alongside strategy leads to ensure that the technical prerequisites for achieving fully autonomous agentic operations are met on the twelve-month roadmap. • Oversee the technical integration and scaling of deployed AI agents within specific operational workflows (e.g., automating specific GTM tasks). • Ensure seamless deployment with rigorous validation testing, monitoring agent performance, and iterating on technical builds to improve decision accuracy and reduce latency. • Actively use AI in day-to-day work, identify where AI can change the shape of problems, and grow fluency as tools evolve.

• Drive the full lifecycle development — from design to deployment — of scalable chatbots and virtual assistants utilizing Microsoft Bot Framework, Kore.ai, or similar CAI technologies • Collaborate with cross-functional teams (product managers, designers, UX/UI specialists) to gather requirements, design conversational flows, and implement user stories • Integrate Natural Language Processing (NLP) and Natural Language Understanding (NLU) techniques to enable chatbots to understand user intent and respond accordingly • Leverage Generative AI services to enhance chatbot responses with creativity and personalization • Develop chatbots for deployment across multiple channels (web, mobile apps, messaging platforms) ensuring a seamless user experience • Conduct thorough testing and debugging to ensure high performance and accuracy of conversational AI solutions • Monitor and analyze user interactions and chatbot performance to identify areas for improvement and optimization • Stay updated on the latest advancements in Conversational AI technologies and best practices

• Design, implement, and optimize the data infrastructure that supports AI and machine learning (ML) solutions • Ensure data availability, quality, and security • Ingest and transform data for the data lake and Feature Store • Develop and support the operationalization of AI and ML models • Conduct complex analyses and monitor data quality