We are looking for a
Principal Backend Engineer to design and build the backend and GraphQL API architecture for AI-powered retrieval systems (RAG) and AI agent orchestration. You’ll develop high-performance GraphQL APIs that serve LLM-driven search, vector retrieval, and graph-based recommendations to users in real time. You’ll also develop frameworks and orchestration to coordinate autonomous AI agents at scale.
This role requires expertise in those areas to help
architect and implement the right solutions for AI-driven retrieval. You’ll work closely with the
Head of Engineering to design scalable AI-powered APIs and retrieval pipelines while remaining
hands-on with coding.
Tech Stack: Golang (preferred), Python, GraphQL, PostgreSQL, Redis, Kubernetes, Azure, Vector Databases (Weaviate, Pinecone, FAISS), Graph Databases (Neo4j, AWS Neptune, TigerGraph), AI Agent frameworks (LangGraph, AutoGen, CrewAI, etc)
Why This Role?
- Architect + Code – You’ll actively design GraphQL APIs for AI retrieval and an AI agent platform while writing production-grade code daily.
- AI-Powered Search & Retrieval (RAG) – Leverage your experience with vector search and knowledge graphs to define future system architecture.
- Scalability & Performance – Ensure low-latency, high-scale orchestration and retrieval in a GraphQL-first environment.
Responsibilities
- Design, build, and optimize GraphQL APIs to serve AI-powered search and retrieval systems.
- Design, build and optimize AI agent platform and framework to orchestrate autonomous agents.
- Hands-on coding (70%), focusing on Golang & Python API development.
- Architect GraphQL queries, mutations, and resolvers to support LLM-powered recommendations.
- Optimize GraphQL query performance (batching, pagination, caching, Dataloader optimizations).
- Guide and implement vector search and knowledge graph capabilities (Weaviate, Pinecone, FAISS, Neo4j, AWS Neptune, TigerGraph).
- Expose RAG and GraphRAG retrieval systems through GraphQL API endpoints.
- Database design for customer-facing access patterns.
- Ensure GraphQL schema design is flexible, scalable, and AI-friendly.
- Implement security best practices (OAuth, JWT, role-based access in GraphQL).
- Collaborate with ML/AI engineers to expose LLM models, embeddings, and knowledge graphs via APIs.
- Deploy and scale services in Azure (Kubernetes, Terraform, AKS).
What We’re Looking For
- 5+ years of backend development experience, with strong Golang (preferred) and Python skills.
- Proven experience designing and optimizing GraphQL APIs for high-performance applications.
- Strong understanding of AI retrieval systems, including RAG, GraphRAG, vector search, and knowledge graphs (even if not currently in use).
- Exposure to AI agent frameworks and design patterns.
- Deep expertise in distributed systems, microservices, and GraphQL performance tuning.
- Experience integrating AI-driven APIs with GraphQL queries and resolvers.
- Azure cloud experience (AKS, Functions, Blob Storage, CosmosDB).
- Proven track record of hands-on coding while also defining backend architecture and best practices.
Bonus Points
- Experience with GraphQL federation, schema stitching, or Apollo Gateway.
- Familiarity with GraphQL + WebSockets (subscriptions for real-time AI updates).
- Exposure to MLOps and model-serving platforms (AWS SageMaker/Bedrock, ClearML, Triton).
