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

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Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.