Senior AI Engineer (Backend & AI Systems)

Role

Join a small founding team to develop the core backend and AI capabilities of a modular AI platform. The position focuses on designing high-performance Python services, implementing gRPC-based APIs for orchestration, integrating Large Language Models, and building scalable data persistence and ingestion pipelines using relational and vector databases.

Key Responsibilities

• Build and guide AI-augmented development of modular, high-performance Python backend services.
• Design, implement, and evolve gRPC-based APIs for orchestrator-to-tool communication.
• Integrate and orchestrate LLMs and third-party LLM APIs to enable tool selection, reasoning, and complex workflows.
• Architect and manage data persistence strategies combining PostgreSQL and vector databases (e.g., ChromaDB).
• Develop and optimize data ingestion and processing pipelines to enrich the AI knowledge base.
• Collaborate with the Lead Architect on core architecture, technical decisions, and platform direction.
• Critically review and integrate AI-generated code for correctness, efficiency, and standards compliance.

Requirements

• 5+ years professional experience building complex, scalable backend systems.
• Expert-level Python proficiency with strong object-oriented design and concurrency knowledge.
• Strong experience designing and building APIs; gRPC experience is a significant advantage. REST/FastAPI experience valued.
• Solid experience with PostgreSQL (or similar relational DBs) and familiarity with ORMs like SQLAlchemy.
• Practical understanding of vector databases (e.g., ChromaDB, Milvus) and their use in AI applications such as RAG.
• Hands-on experience with Docker for building, shipping, and running applications.
• Experience integrating with third-party LLM APIs and familiarity with concepts like tool calling and prompt engineering.
• Ability to understand, review, and integrate complex codebases, including AI-generated code.

Bonus (Highly Desirable)

• Demonstrated aptitude for AI-augmented workflows (“Vibe Coding”).
• Deep, hands-on experience building production gRPC ecosystems.
• Experience building end-to-end Retrieval-Augmented Generation (RAG) systems.
• Background in infrastructure automation, DevOps, or systems engineering.

Technical environment

• Primary languages, tools and platforms: Python, gRPC, PostgreSQL, ChromaDB, Milvus, SQLAlchemy, Docker, FastAPI, NVIDIA NIM, LLM APIs.

What We Offer

• Ground-floor impact on a cutting-edge AI product and close collaboration with the project architect.
• High degree of autonomy and flexibility in working hours.
• Option for remote work and a flexible work environment focused on results.