Lead Backend Developer, AI Pods

We are looking for a Lead Backend Developer to deliver secure, scalable AI-driven API services and agentic workflows for our AI Pods. You will connect language models to internal systems, vector databases, and external tools via orchestration frameworks, treating LLMs as distributed components. Responsibilities Design and build backend API services along with LLM orchestration layers Own API contracts and service reliability end-to-end, ensuring AI edge cases fail gracefully Integrate LLMs with disciplined systems engineering, including evaluation, observability, fallback handling, and cost awareness Implement and maintain advanced RAG pipelines, covering document ingestion, chunking, embedding, and retrieval optimization Develop and connect agent tools using LangChain, LangGraph, and potentially MCP (Model Context Protocol) Build dependable, reusable orchestration frameworks and logic for downstream developer consumption Ensure security, privacy, enterprise-grade observability, and test coverage across all backend workflows Contribute to architecture decisions and engineering standards within the pod Collaborate with frontend engineers, data engineers, and infrastructure teams Requirements Proven backend engineering experience (5+ years) centered on microservices and distributed systems Hands-on proficiency in Python to build high-performance backend services and cloud-native APIs Working knowledge of AWS plus containerization with Docker and orchestration via ECS/EKS Strong understanding of RESTful APIs and practical API design experience Solid grasp of secure coding practices, including strong auth/authz fundamentals English proficiency at B2 (Upper-Intermediate) level or higher Nice to have 2+ years of hands-on experience with AI SDKs (OpenAI, Anthropic/Claude, AWS Bedrock, or equivalent) in production Practical experience with vector databases (Amazon Kendra, OpenSearch), embedding strategies, and retrieval systems Hands-on experience with LangChain, LlamaIndex, or LangGraph Familiarity with AI evaluation tooling or real-time APM (LangSmith, Langfuse, Arize) Skills in React and TypeScript Experience deploying to EKS environments at scale Understanding of agent interoperability patterns (MCP) Knowledge of identity/security domain (IAM, CIAM) Flexibility to use secondary languages such as Java, Node.js, or Go