Senior AI Engineer

Summary

Join our team as a Software Engineer and participate in designing and planning software solutions. You will collaborate with professionals and stakeholders, document technical solutions, and advocate for improvements in product quality, security, and performance. Responsibilities include crafting high-quality code, maintaining coding standards, identifying and resolving technical debt, and implementing an LLM stack. You will also contribute to prompt engineering and LLM behavior tuning. This role requires advanced Python and FastAPI experience, deep Langchain knowledge, and strong database skills. Familiarity with AWS and SCRUM methodologies is also essential.

Requirements

• Advanced Python, experienced with FastAPI
• Deep knowledge of Langchain
• Observability and debugging experience (Langfuse preferred)
• Strong database knowledge (SQL, Redis)
• Experience with asynchronous architectures
• Amazon Web Services knowledge
• SCRUM Methodologies practitioner
• Prompt engineering and LLM behavior tuning

Responsibilities

• Participate in the processes of designing and planning software solutions
• Suggest ideas, new solutions, or improvements to the current technology systems
• Collaborate with the Pro and other stakeholders within Engineering (Frontend, UX, etc.)
• Document the technical solutions with diagrams and the necessary documents for easy understanding by the other technical areas of the company
• Advocate for improvements to product quality, security, and performance
• Craft code that meets our internal standards for style, maintainability, and best practices for a high-scale web environment
• Maintain and advocate for these standards through code review
• Recognize impediments to our efficiency as a team ("technical debt"), propose and implement LLM stack (cost, latency, route flow traceability and debugging with Langfuse

Preferred Qualifications

• Experience with Node.js and NestJS
• Understanding of agent proxy architectures
• Familiarity with Websockets and streaming LLM responses
• Knowledge of vector search and RAG techniques (e.g., Pinecone, Qdrant)
• Familiarity with LLM evaluation methods and tools (e.g., LangSmith, OpenAI evals)
• Awareness of LLM-related security and compliance concerns
• Ability to manage cost, token usage, and latency of LLM calls
• Product thinking around user experience with LLM-powered features
• Experience with fine-tuning or hosting open-source models