Staff AI Applications Engineer

CarParts.com•Long Beach, CA

18h•$156,000 - $219,000•Onsite

About The Position

We are investing heavily in AI-powered engineering — building intelligent agents, integrating LLMs into our platform, and developing MCP (Model Context Protocol) servers to orchestrate context-aware automation across our ecommerce stack. This role sits at the intersection of full-stack engineering and applied AI. This is not a traditional full-stack role with AI bolted on. AI is woven into how we build, ship, and operate: 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.

Requirements

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
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 Haves

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

Responsibilities

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
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
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