机器学习运维架构师
ML Ops Architect
Tiger Analytics 是一家先进的分析咨询公司。我们是多家财富 100 强公司的可信赖分析合作伙伴，帮助他们从数据中创造商业价值。我们的顾问在数据科学、机器学习和人工智能领域拥有深厚的专业知识。我们的业务价值和领导力得到了包括 Forrester 和 Gartner 在内的多家市场研究机构的认可。
我们正在寻找一位积极进取、充满热情的机器学习工程师加入我们的团队。
职位描述：
作为高级 ML OPS 工程师，您将加入一支经验丰富的机器学习工程师团队，支持、构建并推动组织内机器能力的发展。您将与内部客户和基础设施团队紧密合作，打造我们下一代数据科学工作台、ML 平台及产品。您将在与内部利益相关者密切合作的过程中，进一步拓展您的知识，并发展在现代机器学习框架、库和技术方面的专业技能，以理解不断变化的业务需求。如果您热衷于创造性解决方案，并享受在动手、协作的环境中工作，那么这个职位适合您。
要求
您在该职位中的职责包括：
- 利用基于云的架构、技术和平台，构建可扩展且可靠的系统，以处理大规模模型推理。
- 在生产环境中部署和管理机器学习和数据管道。
- 开发模型部署的容器化和编排解决方案。
- 参与快速迭代周期，适应不断变化的项目需求。
- 作为跨职能敏捷团队的一员，创建和增强支持最先进大数据和 ML 应用的软件。
- 践行 CICD 最佳实践，包括测试自动化和监控，以确保 ML 模型和应用代码的成功部署。
- 确保所有代码得到良好管理，以减少漏洞，从风险角度对模型进行良好治理，并遵循负责任和可解释 AI 的最佳实践。
- 与数据科学家、软件工程师、数据工程师和其他利益相关者合作，开发和实施 MLOps 的最佳实践，包括 CI/CD 管道、版本控制、模型版本管理、监控、警报和自动化模型部署。
- 管理和监控机器学习基础设施，确保高可用性和性能。
- 实现强大的监控和日志解决方案
查看英文原文
Tiger Analytics is an advanced analytics consulting firm. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. Our consultants bring deep expertise in Data Science, Machine Learning, and AI. Our business value and leadership have been recognized by various market research firms, including Forrester and Gartner.
We are looking for a motivated and passionate Machine Learning Engineers for our team.
Job Description:
As a Senior ML OPS Engineer, you will be joining a team of experienced Machine Learning Engineers that support, build, and enable Machine capabilities across the organization. You will work closely with internal customers and infrastructure teams to build our next generation data science workbench and ML platform and products. You will be able to further expand your knowledge and develop your expertise in modern Machine Learning frameworks, libraries and technologies while working closely with internal stakeholders to understand the evolving business needs. If you have a penchant for creative solutions and enjoy working in a hands-on, collaborative environment, then this role is for you.
Requirements
What you'll do in the role:
- Implement scalable and reliable systems leveraging cloud-based architectures, technologies and platforms to handle model inference at scale.
- Deploy and manage machine learning & data pipelines in production environments.
- Work on containerization and orchestration solutions for model deployment.
- Participate in fast iteration cycles, adapting to evolving project requirements.
- Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.
- Leverage CICD best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
- Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.
- Collaborate with Data scientists, software engineers, data engineers, and other stakeholders to develop and implement best practices for MLOps, including CI/CD pipelines, version control, model versioning, monitoring, alerting and automated model deployment.
- Manage and monitor machine learning infrastructure, ensuring high availability and performance.
- Implement robust monitoring and logging solutions for tracking model performance and system health.
- Monitor real-time performance of deployed models, analyze performance data, and proactively identify and address performance issues to ensure optimal model performance.
- Troubleshoot and resolve production issues related to ML model deployment, performance, and scalability in a timely and efficient manner.
- Implement security best practices for machine learning systems and ensure compliance with data protection and privacy regulations.
- Collaborate with platform engineers to effectively manage cloud compute resources for ML model deployment, monitoring, and performance optimization.
- Develop and maintain documentation, standard operating procedures, and guidelines related to MLOps processes, tools, and best practices.
Basic Qualifications:
- Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field.
- Typically requires 7+ years of hands-on work experience developing and applying advanced analytics solutions in a corporate environment with at least 4 years of experience programming with Python.
- At least 3 years of experience designing and building data-intensive solutions using distributed computing.
- At least 3 years of experience productionizing, monitoring, and maintaining models
Must have skills:
- Understanding of Azure stack like Azure Machine Learning, Azure Data Factory, Azure Databricks, Azure Kubernetes Service, Azure Monitor, etc.
- Demonstrated expertise in building and deploying AI/Machine Learning solutions at scale leveraging cloud such as AWS, Azure, or Google Cloud Platform.
- Experience in developing and maintaining APIs (e.g.: REST).
- Experience specifying infrastructure and Infrastructure as a code (e.g.: Ansible, Terraform).
- Experience in designing, developing & scaling complex data & feature pipelines feeding ML models and evaluating their performance.
- Ability to work across the full stack and move fluidly between programming languages and MLOps technologies (e.g.: Python, Spark, DataBricks, Github, MLFlow, Airflow).
- Expertise in Unix Shell scripting and dependency-driven job schedulers.
- Understanding of security and compliance requirements in ML infrastructure.
- Experience with visualization technologies (e.g.: RShiny, Streamlit, Python DASH, Tableau, PowerBI).
- Familiarity with data privacy standards, methodologies, and best practices.
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.
Originally posted on Himalayas