AI Architect

该职位来源于猎聘 Roles and Responsibilities: AI Expertise:

• AI Architecture & Solution Design
• Lead end-to-end architecture for AI/ML solutions, from requirements gathering to production rollout.
• Design modular, reusable reference architectures and blueprints for:
• AI Agents and agentic workflows o Retrieval-Augmented Generation (RAG) systems o Traditional ML pipelines
• Research and apply state-of-the-art AI/ML techniques, including generative AI, LLMs, and orchestration frameworks.
• Champion ML/Ops & LLM/Ops best practices, including:
• Infrastructure as Code o CI/CD for models o Observability and automated monitoring
• Be able to oversee the full lifecycle of AI projects: research, prototyping, deployment, optimization.
• Manage technical risks, ensuring scalability, security, and compliance.
• Provide technical recommendations to resolve challenges and enhance system performance. Better with:
• Hands-on experience with Generative AI and LLM technologies, including: o Building agent workflows that orchestrate LLMs, knowledge bases, APIs, and enterprise services. o Prototyping, fine-tuning, and optimizing LLMs using: o Supervised Fine-Tuning (SFT) o Reinforcement Fine-Tuning (RFT) o Distillation, quantization, and prompt engineering best practices.
• Proficiency with GenAI platforms and frameworks: o OpenAI, Azure AI, AWS Bedrock, Google Vertex AI o LangChain/LangGraph, LlamaIndex, Semantic Kernel, Haystack, and related ecosystems. Minimum Job Requirements: Education & Experience
• Master’s or PhD’s degree in Computer Science, Engineering, Information Systems, or related fields.
• 5+ years of experience in software engineering.
• Specialized in both Python & Java coding.
• Proven experience delivering enterprise-grade software products in agile environments.
• Adept at translating ambiguous business needs into tangible technical deliverables.
• English can be used as working language. Preferred (not a must): AI projects experience.
• Understanding of GenAI architectures (LLMs, retrieval pipelines, vector stores) and enterprise integration patterns.
• Familiarity with modern DevOps/CI-CD toolchains, cloud platforms (Azure preferred), and API-driven microservices.
• Familiarity with MLOps, LLMOps, or similar AI delivery frameworks.