高级机器学习工程师（GCP）
Senior Machine Learning Engineer (GCP)
Tiger Analytics 正在寻找一位技能娴熟且富有创新精神的机器学习工程师，要求具备 Google Cloud Platform (GCP) 和 Vertex AI 的实战经验，负责设计、构建和部署可扩展的机器学习解决方案。您将在实现机器学习模型的生产化以及推动端到端的机器学习生命周期中发挥关键作用，从数据摄入到模型服务和监控。
主要职责：
- 使用 Vertex AI 进行机器学习模型的开发、训练和优化，包括 Vertex Pipelines、AutoML 和自定义模型训练。
- 设计并构建可扩展的机器学习流水线，用于特征工程、训练、评估和部署。
- 使用 Vertex AI 端点将模型部署到生产环境，并与下游应用程序或 API 集成。
- 与数据科学家、数据工程师和 MLOps 团队合作，实现可复现且可靠的机器学习工作流。
- 监控模型性能，并设置告警、重新训练触发器和漂移检测机制。
- 在机器学习工作流中使用 GCP 服务，如 BigQuery、Dataflow、Cloud Functions、Pub/Sub 和 GCS。
- 使用 Vertex AI Pipelines、Cloud Build 和 GitOps 实践，将 CI/CD 原则应用于机器学习模型。
- 在 Vertex AI 中实现模型治理、版本控制、可解释性和安全最佳实践。
- 清晰地记录架构决策、工作流和模型生命周期，供内部利益相关者参考。
要求
1. 高级生成式 AI
- 高级 RAG，包括基于图的混合检索
- 多模态代理
- 对 ADK、Langchain 代理框架有深入理解
- 微调和蒸馏
2. Python 专家技能
- 精通 Python，具备扎实的面向对象编程和函数式编程能力
- 熟练使用 ML/DL 库：TensorFlow、PyTorch、scikit-learn、pandas、NumPy、PySpark
- 具备生产级代码编写、测试和性能优化经验
3. GCP 云架构与服务
- 熟练使用 GCP 服务，例如：
- Vertex AI
- BigQuery
- Cloud Storage
- Cloud Run
- Cloud Functions
- Pub/Sub
- Dataproc
- Dataflow
- 理解 IAM 和 VPC
6. API 开发与集成
- 使用 FastAPI 或 Flask 设计和构建 RESTful API
- 将机器学习模型集成到 API 中以实现实时推理
- 实现身份验证、日志记录和性能优化
7. 系统设计与可扩展性
- 设计具有可扩展性和容错性的端到端 AI 系统
- 具备开发分布式系统、微服务和异步处理的实际经验
福利
此职位
查看英文原文
Tiger Analytics is looking for a skilled and innovative Machine Learning Engineer with hands-on experience in Google Cloud Platform (GCP) and Vertex AI to design, build, and deploy scalable ML solutions. You will play a key role in operationalizing machine learning models and driving the end-to-end ML lifecycle, from data ingestion to model serving and monitoring.
Key Responsibilities:
- Develop, train, and optimize ML models using Vertex AI, including Vertex Pipelines, AutoML, and custom model training.
- Design and build scalable ML pipelines for feature engineering, training, evaluation, and deployment.
- Deploy models to production using Vertex AI endpoints and integrate with downstream applications or APIs.
- Collaborate with data scientists, data engineers, and MLOps teams to enable reproducible and reliable ML workflows.
- Monitor model performance and set up alerting, retraining triggers, and drift detection mechanisms.
- Utilize GCP services such as BigQuery, Dataflow, Cloud Functions, Pub/Sub, and GCS in ML workflows.
- Apply CI/CD principles to ML models using Vertex AI Pipelines, Cloud Build, and GitOps practices.
- Implement model governance, versioning, explainability, and security best practices within Vertex AI.
- Document architecture decisions, workflows, and model lifecycle clearly for internal stakeholders.
Requirements
1. Advanced Generative AI
- Advanced RAG including Graph based hybrid retrieval
- Multimodal agent
- Deep knowledge on ADK , Langchain Agentic Frameworks
- Fine tuning and Distillation
2. Python Expertise
- Expert in Python with strong OOP and functional programming skills
- Proficient in ML/DL libraries: TensorFlow, PyTorch, scikit-learn, pandas, NumPy, PySpark
- Experience with production-grade code, testing, and performance optimization
3. GCP Cloud Architecture & Services
- Proficiency in GCP services such as:
- Vertex AI
- BigQuery
- Cloud Storage
- Cloud Run
- Cloud Functions
- Pub/Sub
- Dataproc
- Dataflow
- Understanding of IAM, VPC
6. API Development & Integration
- Designs and builds RESTful APIs using FastAPI or Flask
- Integrates ML models into APIs for real-time inference
- Implements authentication, logging, and performance optimization
7. System Design & Scalability
- Designs end-to-end AI systems with scalability and fault tolerance in mind
- Hands-on experience in developing distributed systems, microservices, and asynchronous processing
Benefits
This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.
Originally posted on Himalayas