AI/ML Engineer - freelancer

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Tasks: Build and continuously evolve shared AI capabilities, reusable services, and engineering accelerators that can be adopted across multiple construction software products. Design and implement production-grade AI systems including LLM-based experiences, retrieval pipelines, tool use, orchestration layers, and agentic workflows. Create human- and machine-executable technical artifacts such as specs, evals, interface definitions, and workflow contracts that improve clarity, speed, and quality of delivery. Define and apply evaluation-driven development loops for AI systems, covering quality, reliability, latency, cost, grounding, and other relevant performance dimensions. Partner with product teams to identify high-value cross-product opportunities, support integration of shared capabilities, and recommend patterns without owning product-specific architecture decisions. Contribute to AI-native engineering practices inside the team through Spec-Driven Development, AI-assisted development workflows, automated testing, and intelligent engineering agents. Apply practical standards and guardrails for AI reliability, observability, governance, security, and responsible production use of AI solutions delivered by the central team. Prototype and validate new technical approaches quickly, turning emerging AI capabilities into scalable building blocks with clear business relevance. Support and guide other engineers through technical collaboration, peer review, and knowledge sharing in modern AI engineering and experimentation. Work closely with AI PM / PO, engineering, and business stakeholders to align implementation choices with customer value, delivery feasibility, and measurable outcomes. What We're Looking For: At least 5+ years of hands-on software engineering experience, including significant work on production-grade AI or data-intensive systems in complex delivery environments. Excellent Python programming skills. Proficiency in Azure / AWS cloud platforms. Hands-on experience with Generative AI technologies and applications. Strong practical understanding of LLMs, retrieval-augmented generation, tool use, agents, and their real-world trade-offs, limitations, and failure modes. Experience designing and operating end-to-end AI systems across APIs, application services, data pipelines, retrieval layers, evaluation workflows, and cloud infrastructure. Ability to define measurable quality criteria and evaluation approaches for AI systems, balancing customer value with reliability, latency, cost, and maintainability. Practical experience with AI-assisted engineering, Spec-Driven Development, and modern developer tooling; able to improve how teams work by combining human judgment with automation and agent support. Strong architectural judgment with the ability to design reusable patterns and shared capabilities while collaborating effectively with product teams that own their local implementation decisions. Demonstrated track record of contributing to cross-functional work from early exploration and prototyping through production rollout and continuous improvement in agile environments. Ability to support and influence other engineers, raise technical standards through collaboration, and create clarity in ambiguous and fast-changing AI environments. Strong communication and collaboration skills, with the ability to translate technical complexity into decisions and actions for product, engineering, and business stakeholders. Adaptive mindset, high learning agility, and genuine openness to continuous change in a field shaped by rapid advances in AI. Master's degree in Computer Science, Software Engineering, Artificial Intelligence, or a related technical field; equivalent experience considered for exceptional candidates. Missing one or two of these qualifications? We still want to hear from you! If you bring a positive mindset, we'll provide an environment where you feel valued and empowered to learn and grow. Originally posted on Him

任务：构建并持续演进可跨多个建筑软件产品采用的共享 AI 能力、可复用服务和工程加速器。设计并实现生产级 AI 系统，包括基于 LLM 的体验、检索流水线、工具使用、编排层和智能体工作流。创建人类和机器均可执行的技术制品，如规格、评估、接口定义和工作流契约，以提升交付的清晰度、速度和质量。为 AI 系统定义并应用评估驱动的开发循环，覆盖质量、可靠性、延迟、成本、 grounding 及其他相关性能维度。与产品团队合作，识别高价值的跨产品机会，支持共享能力的集成，并推荐模式，但不拥有产品特定的架构决策。通过 Spec-Driven Development、AI 辅助开发工作流、自动化测试和智能工程代理，为团队内部的 AI 原生工程实践做出贡献。为中央团队交付的 AI 解决方案在可靠性、可观测性、治理、安全性和负责任的生产使用方面，应用实用的标准和护栏。快速原型化并验证新的技术方法，将新兴 AI 能力转化为具有明确业务相关性的可扩展构建块。通过技术协作、同行评审和在现代 AI 工程与实验中的知识分享，支持和指导其他工程师。与 AI PM / PO、工程和业务利益相关者紧密合作，使实施选择与客户价值、交付可行性和可衡量成果保持一致。我们寻找的：至少 5 年以上动手软件工程经验，包括在复杂交付环境中从事生产级 AI 或数据密集型系统的重要工作。出色的 Python 编程技能。精通 Azure / AWS 云平台。具有生成式 AI 技术和应用的动手经验。对 LLM、检索增强生成、工具使用、智能体及其现实世界中的权衡、局限性和故障模式有扎实的实践理解。具有设计和运营端到端 AI 系统的经验，涵盖 API、应用服务、数据流水线、检索层、评估工作流和云基础设施。能够为 AI 系统定义可衡量的质量标准和评估方法，在客户价值与可靠性、延迟、成本和可维护性之间取得平衡。具有 AI 辅助工程、Spec-Driven Development 和现代开发者工具的实践经验；能够通过结合人类判断与自动化和代理支持来改善团队工作方式。具有强大的架构判断力，能够设计可复用模式和共享能力，同时与拥有本地实施决策的产品团队有效协作。在敏捷环境中，具有从早期探索和原型化到生产推广和持续改进，为跨职能工作做出贡献的可靠记录。能够支持和影响其他工程师，通过协作提升技术标准，并在模糊且快速变化的 AI 环境中创造清晰度。强大的沟通和协作技能，能够将技术复杂性转化为产品、工程和业务利益相关者的决策和行动。适应性思维、高学习敏捷性，以及对由 AI 快速进步所塑造的领域持续变化的真正开放态度。计算机科学、软件工程、人工智能或相关技术领域的硕士学位；对于杰出候选人，同等经验亦可考虑。缺少其中一两项资格？我们仍然希望听到你的声音！如果你带来积极的心态，我们将提供一个让你感到被重视并有权学习和成长的环境。最初发布于 Him

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