AI Architect-杭州

Design and own the overall AI architecture, including model training pipelines, inference systems, data flows, and platform components Lead technical strategy and solution design for large‑scale AI initiatives, including LLMs, multimodal models, knowledge systems, and intelligent agents Architect and optimize model training, fine‑tuning (e.g., LoRA, QLoRA), evaluation, deployment, and monitoring workflows Build and maintain MLOps infrastructure, including CI/CD pipelines, model registries, feature stores, and observability systems Collaborate with product, engineering, and data teams to translate business requirements into scalable AI solutions Evaluate and integrate emerging AI technologies such as RAG, vector databases, model compression, inference acceleration, and AIGC Ensure AI systems comply with security, privacy, governance, and ethical standards Provide technical leadership, mentorship, and architectural guidance to engineering and data science teams Drive continuous improvement in AI system performance, cost efficiency, and reliability

Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or related fields 5+ years of experience in machine learning, deep learning, or AI engineering, with 2+ years in architecture or technical leadership Strong understanding of deep learning fundamentals, including Transformer architectures, LLMs, embeddings, and retrieval‑augmented systems Proficiency with AI frameworks such as PyTorch, TensorFlow, or JAX Hands‑on experience with distributed training, model optimization, and GPU/accelerator‑based compute environments Solid knowledge of cloud‑native technologies (Docker, Kubernetes), MLOps practices, and scalable system design Familiarity with data platforms, including data lakes, data warehouses, and vector databases (e.g., Milvus, Pinecone, FAISS) Excellent communication skills and the ability to influence cross‑functional teams Preferred Qualifications Experience with LLM fine‑tuning, prompt engineering, or building RAG‑based applications Experience developing AI agents, AIGC applications, or enterprise‑grade conversational systems Experience with distributed training frameworks (DeepSpeed, Megatron‑LM, Ray, Horovod) Experience with cloud AI platforms (Azure AI, AWS SageMaker, GCP Vertex AI) Contributions to open‑source AI projects or publications in AI/ML conferences

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