Full Stack Developer: Agentic Systems

About the Role

We are looking for a Full Stack Developer: Agentic Systems to build the product layer for AI-native workflows.

This role focuses on turning LLMs, agents, memory, and external tools into reliable, production-grade user experiences. You will design and ship systems where agents can plan, execute multi-step tasks, recover from failures, maintain context, and deliver consistent value across sessions.

Responsibilities

• Build end-to-end product features across frontend, backend, and AI integrations
  
• Design agent workflows that support planning, tool use, failure handling, and recovery
  
• Integrate LLMs, memory, RAG systems, and external tools into production systems
  
• Build real-time AI interactions using streaming, partial results, and low-latency responses
  
• Improve reliability, observability, fallback logic, and production behavior of AI workflows
  
• Collaborate with ML, backend, product, and design teams to ship features from concept to production
  
• Iterate on AI workflows based on user behavior, evaluation results, and observed failure modes
  
• Establish reusable patterns for building scalable agentic systems
  

Requirements

• Strong full stack engineering experience across frontend and backend development
  
• Solid understanding of system design, APIs, and production-grade architecture
  
• Experience building with LLMs, RAG systems, agents, or AI-powered applications
  
• Ability to work through ambiguity and make pragmatic engineering decisions
  
• Strong ownership mindset with experience taking features from idea to production
  

Tech Stack & Skills

Core Engineering

• Next.js, Node.js, Python
  
• SQL and NoSQL databases
  
• API design and backend architecture
  
• Docker
  

AI & Agentic Systems

• LLM integration using OpenAI, Anthropic, or open-source models
  
• Agent workflows, tool use, memory, or RAG-based systems
  
• Streaming responses and real-time AI interaction patterns
  
• Agent frameworks: LangChain, LlamaIndex, CrewAI, AutoGen, or similar
  
• Vector databases: Pinecone, Weaviate, Qdrant, Milvus, or pgvector
  

Production & Reliability

• Observability, reliability, fallback handling, and debugging in production
  
• Experience with evaluation frameworks for LLM or agent performance
  
• Experience with workflow orchestration systems
  

Nice to Have

• Familiarity with prompt engineering, retrieval strategies, and context management
  
• Experience building AI products beyond chat-based interfaces