Job Overview: We are building innovative products and internal platforms powered by LLM-based agents to automate complex workflows. This role is ideal for an engineer who has gone beyond basic prompting and can design, build, and deploy production-grade agent systems.
Duties and Responsibilities: Design and build LLM-based agent systems using frameworks such as LangGraph, LangChain, CrewAI, or custom solutions. Develop tool-use layers and MCP-compatible interfaces to connect agents with external systems and APIs. Build and manage orchestration pipelines, including multi-agent coordination, task routing, state management, and error handling. Integrate LLM capabilities into product features, enabling autonomous workflows beyond traditional chatbot functionality. Monitor, evaluate, and optimize agent performance (accuracy, reliability, latency, and cost). Collaborate with product and engineering teams to translate business use cases into scalable agent architectures.
Required Qualifications: Minimum of 3 years’ experience in software engineering. Proven experience building and deploying production-level LLM agent systems (beyond proof-of-concepts). Strong proficiency in Python, including async programming and event-driven architectures. Hands-on experience with at least one agent orchestration framework (LangGraph strongly preferred). Solid understanding of prompt engineering, context management, and LLM limitations. Experience with tool/function calling, Retrieval-Augmented Generation (RAG), and structured output handling. Strong analytical skills with the ability to evaluate and improve agent performance systematically.
Educational Background: Not specified
Experience: Minimum of 3 years’ experience in software engineering
Languages: Not specified
Additional Notes: Nice to Have: Experience with Model Context Protocol (MCP) tooling. Familiarity with vector databases such as Pinecone, Weaviate, or pgvector. Exposure to multi-agent systems or autonomous workflow design.
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