Building a sophisticated, modular AI platform designed to automate and reason through complex business and technical challenges, creating a powerful and extensible AI assistant.
Requirements
- Expert-level Python proficiency, with a deep understanding of object-oriented design, concurrency, and best practices.
- Strong, demonstrable experience designing and building APIs (gRPC is a significant advantage; extensive REST/FastAPI experience is also highly valued).
- Solid experience with PostgreSQL (or similar relational databases) and proficiency with an ORM like SQLAlchemy.
- Practical understanding of Vector Databases (e.g., ChromaDB, Milvus) and their role in AI applications like RAG.
- Hands-on mastery of Docker for building, shipping, and running applications.
- Experience integrating with third-party LLM APIs and a solid grasp of concepts like tool calling and prompt engineering.
- A forward-thinking mindset with a demonstrable ability to understand, review, and integrate complex codebases, including those generated with AI assistance.
Responsibilities
- Guide the AI-augmented development of high-performance, modular Python services that form the backbone of our ecosystem.
- Implement and evolve the gRPC-based APIs that enable seamless, efficient communication between the orchestrator and its various tools.
- Integrate and harness Large Language Models to drive complex workflows, tool selection, and advanced reasoning capabilities.
- Architect and manage our data persistence layer, combining the strengths of relational (PostgreSQL) and vector (ChromaDB) databases.
- Build and optimize the data ingestion and processing pipelines that continuously enrich our AI's knowledge base.
- Work directly with the Lead Architect to solve challenging technical problems, make key design decisions, and guide the platform's technical direction.
Other
- 5+ years of professional experience building complex, scalable backend systems.
- A strong aptitude for "Vibe Coding" is a significant advantage. We are looking for someone who is not just open to, but genuinely excited about, an AI-augmented development workflow.
- Deep, hands-on experience designing and implementing a production gRPC ecosystem.
- Experience building end-to-end Retrieval-Augmented Generation (RAG) systems.
- Background in infrastructure automation, DevOps, or systems engineering.
