Lead AI Engineer

We are seeking a Lead AI Engineer (LLMs & Data Pipelines) to drive the design, integration, and operational excellence of LLM-powered capabilities across our platforms.In this role, you will build intelligent features such as classification, extraction, summarization, and action orchestration powered by large language models. You will design embedding and retrieval pipelines (RAG, semantic search), create robust data pipelines for training and evaluation, and define clear evaluation metrics and quality gates to ensure reliable LLM behavior in production.You will work hands-on with inference runtimes such as ONNX Runtime and TensorFlow Lite, benchmarking performance across CPU, GPU, NPU, and DSP environments, and optimizing deployments for latency, cost, and reliability—including in constrained or embedded systems. Collaborating with engineering, data, and MLOps teams, you will integrate models into real-world APIs and production systems while continuously experimenting with prompts, architectures, and model choices.If you are passionate about turning advanced AI research into scalable, production-ready systems and enjoy balancing performance, accuracy, and operational constraints, this may be your next mission.Overall responsibilities and duties:Build and integrate LLM-powered features (classification, extraction, summarization, actions).Integrate models with inference runtimes (such as ONNX Runtime, TensorFlow Lite / LiteRT).Benchmark and validate model performance across different hardware backends (CPU, GPU, NPU, DSP).Design embedding and retrieval pipelines (RAG, semantic search).Create and maintain data pipelines for training and evaluation.Define evaluation metrics and quality gates for LLM behavior.Optimize inference for latency, cost, and reliability.Integrate models into production systems and APIs.Run experiments to evaluate prompts, models, and architectures.Qualifications:Strong experience with LLMs and NLP systemsHands-on experience with embeddings and vector databasesStrong Python skills and ML frameworksExperience building production data pipelinesSolid understanding of evaluation and regression detectionExperience with RAG architecturesMLOps or monitoring experienceExperience with model calibration and accuracy/latency trade-off analysis.Hands-on experience deploying models on edge or embedded devices (constrained environments)