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. What You'll Do - Production AI System Development - Design and build complex backend AI modules: custom agentic workflows, multi-step reasoning chains, and orchestrated tool-use pipelines across client environments. - Translate architectural blueprints from AI strategists into high-performance, production-ready code with clear error handling, observability hooks, and latency targets. - Own end-to-end delivery of AI components from initial scaffold through deployment, ensuring the system behaves exactly as designed in the client's actual environment. - Use AI coding assistants as a standard part of your workflow to accelerate output without sacrificing code quality or architectural integrity. - Advanced RAG and Retrieval Infrastructure - Architect and optimize RAG pipelines over unstructured, messy, and multi-modal data sources, applying chunking strategies, embedding models, and re-ranking techniques that move retrieval accuracy from acceptable to precise. - Implement vector database solutions based on query patterns, data volume, and latency requirements specific to each engagement. - Diagnose and fix retrieval failures in production: identify whether the problem is in chunking, embedding, indexing, or prompt construction, and fix the right thing. - Agentic Orchestration and Context Engineering - Build and maintain agentic systems with particular attention to tool selection, memory architecture, and failure recovery. - Manage context window constraints deliberately: design systems that stay within limits, degrade gracefully when they approach them, and never silently drop critical information. - Optimize prompt pipelines for latency and cost without degrading output quality, benchmarking before and after each change with data rather than intuition. - Infrastructure, Deployment, and Quality - Own deployment of AI services using modern IaC tooling and containerized environments, ensuring repeatable, auditable deploys across dev and production. - Write tests at the right level of the pyramid: unit tests for deterministic logic, integration tests for pipeline behavior, and evaluation harnesses for non-deterministic AI outputs. - Maintain production systems with structured logging, alerting, and cost monitoring so issues surface before clients notice them. - Internal Tooling and Platform Abstraction - Identify patterns across client engagements and abstract the best implementations into reusable internal tools, libraries, and services that reduce future build time. - Document internal tools clearly enough that another engineer can use them without asking you for help, treating internal documentation as a first-class deliverable. - Actively contribute to Cadre's internal AI platform, participating in architecture reviews and proposing improvements when you see a better way. Qualifications - 3+ years of backend engineering experience, with 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 with strong command of backend architecture patterns: API design, async processing, parallel execution, and secure credential handling. - Infrastructure fluency: you use IaC tools (Terraform or Pulumi), deploy with Docker and Kubernetes. - Systems design instincts that favor abstraction and reuse. - Certified Claude Architect or the demonstrated ability to earn the certification within your first 60 days. - AI-native working style: you use coding assistants as a force multiplier, not as a crutch. - Complexity tolerance that others would describe as unusual. - Ownership orientation: you finish what you start, flag blockers early, and take pride in the quality of systems that have your fingerprints on them. Benefits - Real ownership: You design, build, and ship systems end to end, and your name is on the quality of what gets deployed. - AI-native culture: We use AI to run our own operations. - Access to the frontier: Experiment with new models and capabilities through partnerships with Anthropic, OpenAI, and YC. - Upside: Early team members share in the success they help create. - No bureaucracy: Small pods with clear accountability. Compensation The base pay range for this role is $3,500 USD to $4,500 USD.

• Collaborate with product and engineering teams to support customer onboarding and technical adoption. • Develop, maintain, and own customer-facing GitHub repositories and related codebases. • Design, build, test, and deploy software solutions for scientific computing and data science applications. • Apply software engineering principles to address scientific, research, and computational challenges. • Create, maintain, and improve reusable AI agents, automation workflows, and community assets. • Utilize AI, LLMs, and agent-based technologies to enhance customer solutions and development processes. • Support machine learning and data science workflows through software development and integration efforts. • Contribute to simulation-based projects and computational science initiatives. • Troubleshoot technical issues, identify root causes, and implement effective solutions. • Follow software development best practices, including version control, testing, documentation, and code reviews. • Engage with customers to understand requirements, provide technical guidance, and drive successful outcomes. • Help establish and promote engineering best practices across customer and community projects.

• Collaborate with product and engineering teams to support customer onboarding and technical adoption. • Develop, maintain, and own customer-facing GitHub repositories and related codebases. • Design, build, test, and deploy software solutions for scientific computing and data science applications. • Apply software engineering principles to address scientific, research, and computational challenges. • Create, maintain, and improve reusable AI agents, automation workflows, and community assets. • Utilize AI, LLMs, and agent-based technologies to enhance customer solutions and development processes. • Support machine learning and data science workflows through software development and integration efforts. • Contribute to simulation-based projects and computational science initiatives. • Troubleshoot technical issues, identify root causes, and implement effective solutions. • Follow software development best practices, including version control, testing, documentation, and code reviews. • Engage with customers to understand requirements, provide technical guidance, and drive successful outcomes. • Help establish and promote engineering best practices across customer and community projects.

• Design and evolve scalable, secure, and highly available SaaS applications using C#, .NET, and cloud-native architectures. • Define technical architecture and engineering standards across multiple products and teams. • Lead the adoption of microservices, domain-driven design (DDD), CQRS, event-driven systems, and API-first approaches. • Partner with Product, Engineering, DevOps, and fellow Architects to deliver reliable and maintainable solutions. • Mentor senior engineers and provide technical guidance on architecture, design decisions, and implementation strategies. • Drive improvements in system performance, scalability, observability, and security. • Promote engineering best practices, including code quality, automated testing, and operational excellence. • Champion AI-assisted software development by integrating modern AI tools and workflows into everyday engineering practices.