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