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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff AI Applications Engineer - **Company:** CARPARTS INC. - **Location:** Long Beach, CA, United States - **Salary:** $156,000.0 - $219,000.0 - **Contract:** Permanent contract - **Skills:** HTML, JavaScript (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Application Integration Architecture, Microsoft Azure, Cascading Style Sheets (CSS), Cloud Computing, Code Generation, Code Review, Data Structures, Cursor (Graphical User Interface Elements), Database Design, Software Debugging, Software Design Patterns, Programming Tools, Web Development, PostgreSQL, MongoDB, MySQL, Node.Js, Open Source Technology, Performance Tuning, Software Architecture, Redis, Cloud Services, Next.js, Responsive Web Design, TypeScript, Web Application Frameworks, Web Usability, Openapi, Javascript Build, Google Cloud, GitHub Copilot, ReactJS, Delivery Pipeline, Large Language Models, Multi-Agent Systems, Prompt Engineering, Caching, Backend, Containerization, Webpack, Front End Software Development, React Redux, Restful APIs, Code Restructuring, Docker, Microservices - **Published:** August 10, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=99ed7572d661f652 ## About the Role * Optimize frontend performance: Core Web Vitals, page load time, time-to-first-byte * Design APIs (REST,OpenAPI) that are clean, well-documented, andbackward-compatible * Build shared tooling: CLI utilities, code generators, reusable component libraries, and internal developer tools powered by AI * Improve CI/CD pipelines, containerized builds, and deployment workflows * Participate in architecture decisions, code reviews, and technical design documents * 1+ year hands-on experience integrating LLM APIs (OpenAI, Anthropic, or equivalent) into production or near-production systems * Demonstrated ability to build or extend AI agents that use tool-calling, function execution, and structured output * Proven approach to token budget management: prompt optimization, caching strategies, cost monitoring dashboards, * Contributions to open-source AI tooling or MCP ecosystem, Core Engineering (Required) * 5+ years of experience in full-stack web application development using Node.js, JavaScript, TypeScript, and modern frameworks * Extensive experience building scalable applications and microservices using React, Next.js, Node.js, Express, HTML, and CSS * Hands-on TypeScript across frontend and backend systems * Strong knowledge of RESTful API design andOpenAPIspecifications * Experience designing and integrating APIs, including REST and modern data-fetching patterns * Extensive experience with MySQL, MongoDB, PostgreSQL, and Redis, with solid understanding of data modeling trade-offs * Familiarity with micro-frontend architecture and module federation * Strong experience building performant React applications using hooks and state management (Redux or equivalent) * Experience with cloud-native development using Docker and containerized environments * Experience with CDNs, caching strategies, performance optimization, and security considerations * Strong knowledge of JavaScript build tools (Webpack, Vite, or modern bundlers) * Proficiency with Chrome DevTools and frontend performance profiling * Experience with SPA, PWA, responsive design, and MPA architectures * Strong foundation in data structures, algorithms, and database design * Proven experience in software architecture, design patterns, and engineering best practices AI & Automation (Required) * 1+ year hands-on experience integrating LLM APIs (OpenAI, Anthropic, or equivalent) into production or near-production systems * Demonstrated ability to build or extend AI agents that use tool-calling, function execution, and structured output * Solid understanding of agentic concepts and design patterns: * ReAct(Reason + Act) loops, chain-of-thought planning, and step-by-step task decomposition * Tool orchestration - selecting, invoking, and chaining external tools based on model reasoning * Memory architectures: conversation context, scratchpads, vector-backed long-term recall * Self-correction and reflection - agents that detect errors in their own output and retry * Human-in-the-loop checkpoints, confidence thresholds, and graceful fallback to manual workflows * Multi-agent coordination - delegating subtasks across specialized agents and merging results * Acquaintance or hands-on experience developing agents: * Built, extended, or shipped at least one agent (production, internal tool, or well-scoped prototype) that performs multi-step autonomous tasks * Familiar with agent frameworks such asLangChain,LangGraph,CrewAI,Autogen, Claude Agent SDK, or custom orchestration loops * Comfortable designing agent tool schemas, managing agent state, and debugging non-deterministic agent behavior * Experience designing or contributing to MCP servers or similar context-orchestration layers * Proven approach to token budget management: prompt optimization, caching strategies, and cost monitoring * Comfortable using AI coding assistants (GitHub Copilot, Claude Code, Claude Cowork, Cursor) daily to accelerate development * Able to write effective prompts for code generation, refactoring, test creation, and documentation * Understands foundational LLM concepts: tokens, temperature, context windows, embeddings, and RAG * Can evaluate AI-generated code for correctness, security, and performance - not just accept output blindly Nice to Have * Experience with public cloud services (AWS, Azure, GCP) * Experience with ecommerce/retail purchase journeys * Experience migrating legacy applications to modern stacks * Familiarity with vector databases (Pinecone,Weaviate,pgvector) and RAG pipelines * Experience with agent frameworks (LangChain,LangGraph,CrewAI) or custom orchestration loops * Experience fine-tuning or distilling models for domain-specific tasks * Contributions to open-source AI tooling or the MCP ecosystem * Experience withGraphQL ## Description * Every engineer uses AI coding assistants (Claude Code, Claude Cowork, GitHub Copilot, Cursor) as a daily multiplier - not an optional extra * We are building production AI agents that automate merchandising, search relevance, customer support triage, and content generation * Our agents use agentic patterns - autonomous planning, multi-step reasoning, tool orchestration, self-correction, and human-in-the-loop checkpoints - not simple prompt-response chains * Our platform exposes MCP servers so LLM-powered tools can read catalog data, trigger workflows, and act on real-time signals * We treat prompt engineering and token economics as first-class engineering disciplines, reviewed in PRs alongside application code If you want to ship AI features that millions of customers interact with - not just prototype in a notebook - this is the role. Who You Are * The Builder:You'd rather write a reusable abstraction, a CLI tool, or a code generator than repeat the same manual task twice - and you design systems that scale without you babysitting them. * The Troubleshooter:A user reports a broken checkout flow and you instinctively open the network tabztrace the API call, and pinpoint whether it's a frontend state bug, a backend validation edge case, or a data mismatch - before anyone else finishes reading the ticket. * The AI Enthusiast:You treat token budgets and prompt design with the same rigor as component architecture - optimizing context windows, evaluating model trade-offs, and shipping AI-powered features that move product metrics. What You'll Do Build & Ship Product Features (50%) * Design, develop, and own full-stack features across React/Next.js frontends and Node.js/Express microservices * Build AI-powered product experiences: intelligent search, personalized recommendations, automated content generation, and conversational commerce flows * Design and develop agentic systems - agents that plan, reason over multiple steps, select and call tools, handle errors autonomously, and escalate to humans when confidence is low * Implement agentic patterns:ReActloops, chain-of-thought planning, reflection/self-critique, memory (short-term context and long-term retrieval), and multi-agent coordination * Develop and maintain MCP servers that expose ecommerce domain tools (catalog, pricing, inventory, order) to LLM-powered clients * Integrate LLM APIs into production paths with proper error handling, fallback strategies, andcostguardrails * Write prompts, evaluation harnesses, and monitoring for AI features - treat them as code, version them, review them, * Translate business requirements into technical designs with product and design stakeholders * Mentor engineers on AI integration patterns, prompt engineering, and modern full-stack practices * Stay current with AI/ML tooling, LLM advances, and MCP ecosystem developments - bring what you learn back to the team ## Related Videos - [The Resilience of the World Wide Web](https://www.wearedevelopers.com/videos/1281-the-resilience-of-the-world-wide-web) - [Reducing LLM Calls with Vector Search Patterns - Raphael De Lio (Redis)](https://www.wearedevelopers.com/videos/1714-reducing-llm-calls-with-vector-search-patterns-raphael-de-lio-redis) - [MySQL Protocol Features You Should Be Aware Of](https://www.wearedevelopers.com/videos/100267-mysql-protocol-features-you-should-be-aware-of) - [Building AI Applications with LangChain and Node.js](https://www.wearedevelopers.com/videos/1512-building-ai-applications-with-langchain-and-node-js) - [Coding for Good: Achieving social change with an app](https://www.wearedevelopers.com/videos/1645-coding-for-good-achieving-social-change-with-an-app) - [Accelerating Authentication Architecture: Taking Passwordless to the Next Level](https://www.wearedevelopers.com/videos/733-accelerating-authentication-architecture-taking-passwordless-to-the-next-level) ## Related Articles - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [Everything a Developer Needs to Know About MCP with Neo4j](https://www.wearedevelopers.com/magazine/604-everything-a-developer-needs-to-know-about-mcp-with-neo4j) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)