We are seeking a Backend Team Lead to spearhead the development of ludeo.ai, our GenAI-powered product that enables users to generate interactive (gaming experiences) directly from prompts or video content. This is a high-impact leadership role at the intersection of backend architecture, multimodal AI, and real-time systems. You will architect and lead the AI engine that transforms unstructured inputs (text/video) into structured, interactive gaming playable moments.
What Youll Do
Lead & Mentor: Build and manage a high-performing backend/AI engineering team, drive architectural decisions, and foster rapid innovation while maintaining production-grade reliability.
Design AI-Native Systems: Architect scalable microservices powering complex AI workflows. Design and implement Retrieval-Augmented Generation (RAG) pipelines, embedding strategies, and vector database infrastructure (e.g., Pinecone, Weaviate, Milvus, PGVector). Optimize retrieval, prompt orchestration, latency, and cost.
Agentic Workflows: Design multi-agent systems using planner/executor/tool-calling patterns. Implement stateful, multi-step AI workflows with frameworks such as LangChain, CrewAI, AutoGen, or similar. Build evaluation, observability, and safety mechanisms for LLM systems.
Multimodal AI: Integrate multimodal models (vision + text) to understand video and translate it into structured form.
Scale & Infrastructure: Ensure robustness, security, and high availability on AWS/Kubernetes. Design distributed systems that handle real-time data and AI workloads efficiently.
Collaborate: Work closely with Product and Design to translate GenAI capabilities into stable, scalable production features.
Requirements: Expreince leading engineering teams in fast-paced environments with strong ownership and architectural responsibility.
Backend Expertise: 6+ years of backend development experience with deep expertise in Node.js and microservices. Strong distributed systems and API design experience.
GenAI Systems Experience: Hands-on experience building production LLM systems. Proven experience with RAG architectures, vector databases, embedding pipelines, and prompt orchestration. Experience designing multi-step or agentic AI workflows.
Infrastructure: Strong experience with AWS and Kubernetes in production environments. Deep knowledge of SQL & NoSQL systems.
Communication: Ability to translate complex AI systems into clear product and business decisions.
This position is open to all candidates.