AI Application Engineer

AI-Forward Full-Stack Engineer

CarParts.com is the go-to eCommerce platform for auto care and maintenance. We provide drivers with quality parts at competitive prices and enable them to schedule appointments with trusted mechanics directly through our website. Using world-class design principles and the latest technologies, we deliver a fast, intuitive digital experience backed by our company-owned national distribution network. With over 1,000 employees worldwide, we are scaling rapidly, fueled by our most recent strategic partnership and $35 million investment. This positions us for the next phase of growth as we continue to empower drivers along their journey.

At CarParts.com, our culture goes beyond our core values of Safety First, Customer Focused, and Commitment to Excellence. We are a performance-driven, data-focused, and fast-paced team where results matter and winning is expected. - Hungry & Hardworking: We set ambitious goals, measure progress with clear metrics, and hold ourselves accountable to deliver results. - Promote from Within: We reward top performers with opportunities for growth and advancement. - Collaborative & In-Person: We believe the best ideas and fastest execution happen face-to-face. - High Standards: We move quickly, pay attention to details, and dig deep - whether it's analyzing contracts, aggregating complex scenarios, or building clear, data-driven presentations. - No Passengers: We value grit, ownership, and the relentless pursuit of results

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.

Why This Role Is AI-Forward

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.

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

AI & Automation Requirements & Developer Experience (30%)

• 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

Collaborate & Grow (20%)

• 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

Position Requirements

• Contributions to open-source AI tooling or MCP ecosystem

CarParts.com is an equal-opportunity employer. We enthusiastically accept our responsibility to make employment decisions without regard to race, religious creed, color, age, sex, sexual orientation, national origin, religion, marital status, medical condition, physical or mental disability, military service, pregnancy, childbirth and related medical conditions, or any other classification protected by federal, state, and local laws and ordinances.

The above-noted job description is not intended to describe, in detail, the multitude of tasks that may be assigned but rather to give the incumbent a general sense of the responsibilities and expectations of his/her position. As the nature of business demands change so, too, may the essential functions of this position.

Position Requirements

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