AI Agent Product Engineer

Pixlr Group

About The Position

We are looking for a Full Stack Engineer to participate in the complete process of core AI Agent product development, from architectural design and capability building to production deployment. You will be involved in multiple aspects including web products, API services, agent orchestration systems, background task systems, data layers, and deployment processes, working with the team to build AI Agent products for real production environments. You will be deeply involved in the engineering construction of AI Agent systems, including key modules such as Agent Loop, tool calling, task orchestration, multi-step execution, context management, model routing, asynchronous task processing, result persistence, error recovery, log tracking, and system observability. This role is not about simply calling large model APIs, but requires combining LLM capabilities, business processes, front-end and back-end systems, and user experience to build production-grade Agent products that are stable and continuously scalable. In this role, you need to understand how an Agent receives user goals, breaks down tasks, selects tools, performs actions, handles intermediate states, continues reasoning based on feedback, and ultimately produces reliable results. You also need to pay attention to practical engineering issues during Agent execution, such as task timeouts, tool failures, retry strategies, state consistency, cost control, concurrent execution, queue scheduling, data tracking, and the reproducibility of generated results. You will collaborate closely with product, design, front-end, back-end, and infrastructure teams to integrate AI capabilities into real product experiences. We hope you can understand front-end product forms and user interactions, as well as delve into back-end services, data models, task queues, and agent orchestration systems, helping the team build a long-term maintainable, scalable, and observable AI Agent engineering system. This position is suitable for Full Stack Engineers with a strong interest in AI Agents, LLM application engineering, complex web products, and system engineering. We particularly value your solid engineering capabilities, clear system design thinking, and ability to transform complex AI capabilities into stable business functions. Your main responsibilities include: Participating in the full lifecycle development of AI Agent products, including web experience, API services, agent orchestration, tool calling, Agent Loop, task planning, execution feedback, state management, and result persistence. Designing and implementing production-ready Agent product capabilities, including core modules such as API services, task queues, asynchronous workflows, data models, permissions, billing, and business state management. Building and optimizing the invocation link between agents and tool systems, improving execution stability, observability, fault tolerance, and extensibility. Participating in the design and implementation of complex engineering problems such as multi-agent collaboration, tool routing, context management, model selection, task retries, and exception recovery. Participating in web application development and maintenance, completing product features, interaction flows, front-end state management, and user experience optimization. Responsible for the development, optimization, and maintenance of API services, background task systems, data layers, Monorepo engineering, and Docker deployment processes. Collaborating with product, design, and engineering teams to transform AI capabilities into stable, deliverable, and scalable product features. Continuously exploring best practices for AI-assisted development and Agent engineering, outputting high-quality, maintainable, and testable code.

Requirements

Strong proficiency in TypeScript, React, and Next.js, with experience building complete web applications.
Strong proficiency in Node.js, with solid experience designing and building API services.
Experience with Hono, Express, or similar Node.js service frameworks.
Good understanding of AI Agent engineering concepts, including Agent Loop, tool calling, task orchestration, context management, tool execution, state tracking, and result persistence.
Strong experience with PostgreSQL and Prisma for data modeling, query optimization, and business data management.
Experience with Redis / Valkey, BullMQ, or similar queue and caching systems, including async jobs, retry mechanisms, scheduling, and background processing.
Familiarity with Docker-based development and deployment workflows.
Experience with Nx, Turborepo, or similar large-scale TypeScript Monorepo setups.
Strong problem-solving skills, with the ability to independently debug complex system, data, and execution-flow issues.
Experience delivering complete product features in real-world production environments.

Nice To Haves

Experience with Mastra, LangChain, LlamaIndex, AutoGen, CrewAI, or other agent orchestration frameworks.
Experience with RAG, vector search, embeddings, knowledge bases, LLM tool calling, or multimodal generation systems.
Experience with FastAPI, Flask, or Python-based LLM application development.
Experience with NestJS, Fastify, Koa, tRPC, GraphQL, WebSocket, or other Node.js ecosystem technologies.
Experience with AWS or other cloud infrastructure, deployment, monitoring, and operations.
Experience with CI/CD, system monitoring, logging, tracing, performance profiling, alerting, and production reliability work.
Experience designing and building high-concurrency systems, large-scale user-facing systems, or complex background job systems.
Understanding of model routing, cost control, generation task state management, retry design, and result consistency in AI products.
Experience building complex web products, AI products, or platform products from 0 to 1.

Responsibilities

Participate in the full lifecycle of AI Agent product development, including web experience, API services, agent orchestration, tool calling, Agent Loop, task planning, execution feedback, state management, and result persistence.
Design and implement production-ready AI Agent product capabilities, including API services, task queues, async workflows, data models, permissions, billing, and business state management.
Build and optimize the execution flow between agents and tool systems, improving stability, observability, fault tolerance, and extensibility.
Work on complex engineering problems such as multi-agent collaboration, tool routing, context management, model selection, retries, and failure recovery.
Develop and maintain web application features, product workflows, frontend state management, and user experience improvements.
Maintain and improve API services, background job systems, data layers, Monorepo workflows, and Docker-based development/deployment processes.
Collaborate with product, design, and engineering teams to ship stable, scalable, and production-ready AI product features.
Continuously explore best practices for AI-assisted development and Agent engineering, while producing high-quality, maintainable, and testable code.

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