About Gaia
Gaia is building the next generation of sovereign enterprise AI, enabling organizations to securely connect their knowledge, systems, and workflows through intelligent search, AI assistants, and autonomous agents.
We're looking for an AI Developer who can build scalable systems and integrate advanced AI capabilities into real-world enterprise applications.
This is not a traditional machine learning research role. We're looking for someone who knows how to build reliable backend software powered by AI, taking LLMs and agentic workflows from prototypes to production.
What You'll Work On
- Design, develop, and maintain scalable backend services, APIs, and microservices.
- Build and integrate LLM-powered applications, AI assistants, and autonomous agents.
- Develop agentic workflows involving tool calling, function execution, planning, and orchestration.
- Implement RAG pipelines, semantic search, embeddings, reranking, and context retrieval.
- Integrate enterprise platforms and third-party services through MCPs, APIs, webhooks, and connectors.
- Build reliable asynchronous processing, background jobs, and event-driven workflows.
- Optimize AI application performance, latency, reliability, and inference costs.
- Implement authentication, authorization, access controls, and secure data handling.
- Collaborate with AI, product, and infrastructure teams to deliver production-ready features.
Technical Requirements
- Strong proficiency in Python and backend development frameworks such as FastAPI.
- Solid experience designing REST APIs, asynchronous services, and distributed systems.
- Experience with PostgreSQL, Redis/Valkey, and database optimization.
- Hands-on experience integrating LLMs through APIs or self-hosted inference.
- Practical understanding of RAG, embeddings, vector databases, and semantic search.
- Familiarity with agent frameworks, orchestration patterns, and LLM tool/function calling.
- Experience with Docker, Git, CI/CD pipelines, and cloud-based deployments.
- Strong software engineering fundamentals, including testing, debugging, observability, and clean architecture.
Nice to Have
- Experience with vector databases, hybrid retrieval, and reranking pipelines.
- Familiarity with LangGraph, LangChain, LlamaIndex, or custom agent runtimes.
- Knowledge of Kubernetes, Terraform, and cloud platforms (AWS, Azure, GCP, or OCI).
- Experience building multi-tenant SaaS applications and enterprise integrations.
- Understanding of AI evaluation, guardrails, prompt injection defense, and permission-aware retrieval.
- Experience with MCP and AI tool integrations.
