Senior Generative AI Technical Lead

1 week ago


tiruppur, India beBeeAIEngineer Full time

About This Role We are seeking an exceptional Senior AI Engineer to lead the design and development of cutting-edge Generative AI systems. The ideal candidate will have a strong background in backend or API development, with a proven track record in LLM-based application design and Generative AI workflows. Key Responsibilities: AIArchitectures: Design and develop large-scale Generative AI systems, including intelligent agents and automation workflows. LLMIntegration: Integrate and optimize large language models for reasoning, summarization, and structured content generation. KnowledgeRetrieval: Design and implement retrieval-augmented and context-aware AI pipelines, combining embeddings, semantic search, and hybrid retrieval methods. BackendEngineering: Build robust, scalable backend services and APIs using TypeScript and Python, ensuring high performance, fault tolerance, and clean integration between AI components and backend systems. DataPipelines: Develop and manage pipelines to extract, process, and transform unstructured data into AI-ready formats. InfrastructureDeployment: Design and maintain cloud-native, containerized, and event-driven architectures with Infrastructure-as-Code practices. ModelEvaluationMonitoring: Establish model evaluation metrics, continuous validation workflows, and performance dashboards to ensure production reliability and drift detection. LeadershipMentorship: Lead design discussions, perform technical reviews, and mentor AI engineers, collaborating cross-functionally with product and platform teams. Required Skills: Strong proficiency in Python and TypeScript, with a solid background in backend or API development. Proven experience in LLM-based application design, Generative AI workflows, or AI agent systems. Understanding of retrieval-augmented generation (RAG) and semantic/embedding-based search principles. Experience building scalable cloud-native services, including event-driven or asynchronous architectures. Familiarity with infrastructure automation, container orchestration, and CI/CD pipelines. Exposure to MLOps principles — model lifecycle management, evaluation, and continuous improvement. Strong problem-solving, analytical, and architectural reasoning skills. Excellent communication, collaboration, and mentoring abilities.



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