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
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 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
