Description
As a Lead DevOps Engineer for Cloud Products and Patterns at LSEG, you will play a crucial role in developing and implementing cloud product strategies, solutions, and best practices across the organization, primarily to support machine learning capabilities.
You will leverage your expertise to ensure strategic alignment, foster innovation, and drive the adoption of cutting-edge cloud technologies. Your leadership and vision will empower developers within LSEG to achieve seamless and efficient cloud infrastructure deployments, ensuring robust, secure, and scalable cloud solutions.
Responsibilities:
- Demonstrate extensive knowledge of technologies and provide direction, mentoring, and advice on design, development, and implementation best practices and processes.
- Create and implement technical solution designs and resolve issues.
- Build and demonstrate a breadth of knowledge of various domains within technology, understanding linkages and dependencies of projects and key senior stakeholders.
- Support technical roadmaps for the timely implementation of a technical solution strategy.
- Develop technical strategies and research to enhance and scale machine learning-related cloud product capabilities on Azure and AWS.
- Design, build, and deliver ML models and components that tackle internal use cases.
- Inform ML infrastructure decisions using understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, and validation.
- Evaluate new technologies or techniques, propose patterns for their use, and lead the development of reference code to set a standard.
- Build strong relationships with internal and external customers, growing your network and collaborating with other teams.
- Communicate complex technical information clearly and concisely in an audience-appropriate format.
Requirements:
- Degree in Computer Science Engineering or equivalent work experience.
- Solid understanding of Infrastructure as Code (IAC) concepts.
- Experience with Azure and AWS cloud platforms.
- Experience in leading or developing ML solutions.
- Experience in DevOps practices, especially in machine learning model deployment.
- Hands-on experience with CI/CD pipelines, code scanning tools, secrets management, Docker, and programming languages (Java/JavaScript, C#, Python).
- Experience with version control systems like GitHub or GitLab.
- Deep understanding of architecture principles and design methodologies.
- Proven ability to adapt to new technologies and learn quickly.
- Good communication and presentation skills.
- Experience in Agile/Scrum methodology.