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
