Software Engineer

Requirements:

• 2+ years of total software engineering experience, including hands-on experience building applications using LLMs.
• Strong Python fundamentals and experience building APIs, writing tests, and working with asynchronous or background processing systems.
• Hands-on experience with LLMs, prompt engineering, context engineering, and at least one agentic framework or tool-calling workflow.
• Practical experience building RAG pipelines using embeddings, vector search, retrieval, and context assembly.
• Understanding of relational databases and familiarity with vector databases; exposure to graph databases is a plus.
• Experience evaluating LLM outputs, writing test cases, and debugging quality issues in AI applications.
• Understanding of quality, latency, reliability, and token-cost trade-offs when building LLM-powered features.
• Strong problem-solving skills and the ability to take ownership of assigned tasks, work through ambiguity, and deliver reliable solutions.
• Ability to collaborate effectively with product and engineering teams and communicate technical decisions clearly.
• Curiosity and willingness to learn new AI frameworks, model capabilities, and production engineering practices.

Good to have:

• Experience building and shipping an agentic or multi-step tool-using application.
• Familiarity with SSE or streaming LLM responses.
• Experience with hybrid search, reranking, graph-based retrieval, or advanced retrieval strategies.
• Exposure to LLM evaluation frameworks, golden datasets, and automated regression testing.
• Experience with production observability, tracing, and debugging LLM applications.
• Familiarity with async Python frameworks, distributed systems, and cloud-based AI services.
• Experience working with multiple LLM providers and implementing model integrations.