Experienced Applied AI Engineer — Full-Stack & Production Systems

Role Overview

We’re seeking a hands-on Applied AI Engineer to build, deploy, and maintain AI-powered business applications, automations, and integrations for clients. Projects may include quoting, lead management, operational dashboards, knowledge assistants, AI agents, and connected business workflows. We value demonstrated ownership and production experience over a specific number of years.

Responsibilities

• Translate client needs into clear requirements, technical designs, estimates, and measurable acceptance criteria.
• Build full-stack applications spanning user interfaces, backend services, databases, APIs, integrations, and AI workflows.
• Use AI coding tools to accelerate development while reviewing generated code and maintaining quality, security, and reliability.
• Deploy and operate applications, establishing appropriate testing, monitoring, logging, backup, and recovery practices.
• Maintain production systems, investigate incidents, improve performance, and scale infrastructure as usage grows.
• Document architecture and operating procedures, and develop reusable components for future projects.
• Build secure integrations and workflows with appropriate permissions, retries, error handling, human approvals, and recovery paths.
• Evaluate AI system quality, monitor usage and costs, and validate changes before release.

Required Skills

• Practical proficiency with Claude Code, Cursor, Codex, or comparable AI coding tools, including task structuring, repository context management, code review, testing, and debugging.
• Strong backend skills in a production stack such as Python or TypeScript/Node.js, plus experience with a modern frontend framework.
• Experience independently building production applications and owning their delivery and maintenance.
• Strong API integration skills, including webhooks, authentication, retries, rate limits, synchronization, and duplicate prevention.
• Strong SQL and relational data modeling skills, including migrations, transactions, indexing, and data integrity.
• Ability to build RAG and knowledge systems, including document ingestion, parsing, chunking, embeddings, retrieval, reranking, citations, index maintenance, and evaluation.
• Experience implementing backend-enforced role-based permissions and separating access among users, departments, and client organizations.
• Ability to build AI agents and orchestrated workflows with managed state, background jobs, timeouts, retries, safeguards, and human approval for sensitive actions.
• Understanding of model selection and routing, structured-output validation, fallbacks, usage limits, and controls for data handling, uncontrolled loops, and costs.
• Experience with structured logging, error tracking, metrics, traces, actionable alerts, and protecting sensitive information in logs.
• Ability to create AI evaluations and tests for answer quality, retrieval, tool correctness, permissions, and failure cases; track changes and detect regressions.
• Sound testing and release practices, including unit, integration, and end-to-end testing; version control; code review; CI/CD; separate environments; safe migrations; and rollback or recovery plans.
• Practical experience with at least one major cloud platform, deployment automation, secrets management, networking, and background processing.
• Knowledge of application and AI security, including authentication, authorization, input validation, secure credentials, audit trails, prompt-injection risks, and narrowly scoped agent access.
• Ability to diagnose performance and cost bottlenecks and apply suitable indexing, caching, batching, and asynchronous processing.
• Strong business judgment and independent execution: clarify requirements, explain tradeoffs, challenge unrealistic scope, communicate blockers, and take responsibility for systems after launch.

Nice to Have

• Experience with quoting, CRM, ERP, lead-management, or operational systems.
• Experience with document extraction, OCR, search, or knowledge-management applications.
• Familiarity with workflow platforms such as n8n, Make, or Zapier alongside custom code.
• Experience building reusable integrations and configurable workflows for multiple clients.
• Experience in a small product team, as a founding engineer, or in a client-facing engineering role.