Lead DevOps Engineer, Cloud Products and Patterns

LSEG•St. Louis, MO

21d

About The Position

As a Machine Learning DevOPS Engineer for Cloud Products Framework group within our Infrastructure & Cloud team, you will play a crucial role in evangelizing and developing 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.

Requirements

Must have a degree in Computer Science Engineering or equivalent work experience
Solid understanding of IAC concepts
Experience with Azure and AWS cloud platform
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 on any one of - GitHub, GitLab
Deep understanding of architecture principles and design methodologies
Proven ability to adapt to new technologies and learn quickly
Good communication skills and presentation skills
Experience in Agile/Scrum methodology

Responsibilities

Demonstrates an extensive knowledge of technologies and/or thought leadership level of a technology, providing direction, mentoring and advising others on design, development and / or implementation best practice and processes
Creates and implements technical solution designs and resolves issues
Continues to build and demonstrates a breadth of knowledge of various domains within technology, understanding the linkages and dependencies of projects/work and the key senior stakeholders
Supports technical roadmaps for the timely implementation of a technical solution strategy
Develop technical strategy and research to enhance and scale machine learning related cloud product capabilities on Azure and AWS
Design, build, and/or deliver ML models and components that tackle internal use cases
Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, and validation.
Evaluates how new technology or techniques may better implement the strategy, proposes patterns for its use and leads the development of reference code to set a standard
Builds strong relationships with internal and external customers, growing own network and collaborating with other teams.
Communicates complex / technical information clearly and concisely in an audience appropriate format.