机器学习工程师
Machine Learning Engineer
Tiger Analytics 是人工智能和高级分析咨询领域的全球领导者，帮助财富 1000 强企业解决最棘手的业务挑战。我们致力于推动 AI 的边界，为更美好的明天提供数据驱动的确定性。我们的 6000 多名技术专家和顾问团队遍布五大洲，规模化构建前沿的机器学习和数据解决方案。加入我们，做出卓越的工作，塑造企业 AI 的未来。
要求
- 5 年以上专业软件开发经验，精通 Python，并熟练应用软件工程和设计原则（面向对象编程、函数式编程、设计模式、测试框架、CI/CD 基础知识）。
- 深入理解基于云的数据平台（Azure、Databricks 等），包括集群配置、Spark 优化技巧和最佳实践。
- 熟悉分布式数据处理系统（Spark、Delta 表、云存储层），并有构建数据流水线、优化性能和处理大规模数据集的实际经验。
- 了解 DevOps 和工程规范实践，如容器化（Docker）、基础设施即代码、CI/CD 流水线和工作流的自动化测试。
- 在跨职能团队（数据科学、数据工程、云运维、产品）中有效工作的能力，具备积极主动、求知若渴和进取的心态。
- 能够将模糊的业务或分析需求转化为可扩展的技术方案，具备扎实的代码质量、可靠性、可观测性和工程最佳实践基础。
附加资格（加分项）：
- 使用生产级 MLOps 框架（MLflow、AzureML、Databricks Model Serving）实现和部署机器学习模型的经验，对模型生命周期管理（版本控制、血缘关系、监控、重新训练流程和部署自动化）有深入理解。
- 熟悉现代数据和 ML 架构模式，如特征存储、向量存储、低延迟推理流水线。
福利
随着公司的发展，有显著的职业发展机会。该职位提供一个独特的机会，加入一个小型、快速成长、具有挑战性和创业精神的环境，拥有高度的个人责任感。
Tiger Analytics 向所有申请人和员工提供平等的就业机会，不因种族、肤色、宗教、年龄等因素而有所区别。
查看英文原文
Tiger Analytics is a global leader in AI and advanced analytics consulting, empowering Fortune 1000 companies to solve their toughest business challenges. We are on a mission to push the boundaries of what AI can do, providing data-driven certainty for a better tomorrow. Our diverse team of over 6,000 technologists and consultants operates across five continents, building cutting-edge ML and data solutions at scale. Join us to do great work and shape the future of enterprise AI.
Requirements
- 5+ years of professional software development experience, with strong proficiency in Python, and applying software engineering and design principles (OOP, functional programming, design patterns, testing frameworks, CI/CD fundamentals).
- Deep understanding of cloud-based data platforms (Azure, Databricks etc.), including cluster configuration, Spark optimization techniques and best practices.
- Strong understanding of distributed data processing systems (Spark, Delta tables, cloud storage layers) with hands-on experience in building data pipelines, optimizing performance, and handling large-scale datasets.
- Exposure to DevOps and engineering hygiene practices such as containerization (Docker), infrastructure-as-code, CI/CD pipelines, and automated testing for workflows.
- Proven ability to work effectively in cross-functional teams (DS, DE, Cloud Ops, Product) with a proactive, inquisitive, and go-getter mindset
- Ability to translate ambiguous business or analytical requirements into scalable technical solutions, with solid grounding in code quality, reliability, observability, and engineering best practices.
Additional qualifications (Nice to have):
- Experience in operationalizing and deploying machine learning models using production-grade MLOps frameworks (MLflow, AzureML, Databricks Model Serving), with a strong understanding of model lifecycle management such as versioning, lineage, monitoring, retraining workflows, and deployment automation.
- Familiarity with modern data and ML architecture patterns such as feature stores, vector stores, low-latency inference pipelines.
Benefits
Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.
Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.
Originally posted on Himalayas