DevOps/MLOps Engineer

at  LSEG London Stock Exchange Group

Job Description:
As a DevOps/MLOps Engineer, you will play a crucial role in the deployment and management of machine learning models and infrastructure. You will collaborate closely with data scientists, Business Architects, and the DevOps team to ensure seamless integration and efficient operations. Your expertise in cloud platforms, CI/CD pipelines, and machine learning frameworks will be essential in driving our projects forward.

Key Responsibilities:

• Check deployment pipelines for machine learning models and ensure smooth operation.
• Review code changes and pull requests from the data science team.
• Trigger CI/CD pipelines after code approvals and monitor their execution.
• Ensure all tests pass and model artifacts are generated and stored correctly.
• Deploy updated models to production following successful pipeline completion.
• Collaborate with data scientists and DevOps teams for integration and deployment.
• Containerize models using Docker and deploy them on cloud platforms (AWS)
• Set up monitoring tools (e.g., DataDog, AWS CloudWatch) to track metrics such as response time, error rates, and resource utilization.
• Establish alerts and notifications to detect anomalies or deviations from expected behavior(knowlage on Moogsoft/BigPanda/ServiceNow/ServiceCloud is added advantage).
• Analyze monitoring data, logs, and system metrics to troubleshoot issues.
• Collaborate with the data science team to develop updated pipelines and address any faults.
• Document changes, optimizations, and troubleshooting processes.

Qualifications:

• Proven experience as a DevOps Engineer or in a similar software engineering role.
• Proficiency in coding with Python and Java.
• Experience deploying applications on cloud platforms (AWS).
• Proven understanding of CI/CD pipelines and Infrastructure-as-Code (IaC) tools (Terraform, Ansible).
• Familiarity with relational and non-relational databases, data warehousing, and data streaming frameworks (Snowflake, DynamoDB, SQL).
• Knowledge of security concepts including firewalls, encryption, VPNs, and secure data transfer.
• Experience with logging tools such as AWS CloudWatch and AWS CloudTrail.
• Strong problem-solving approach and excellent troubleshooting skills.
• Good communication skills for effective collaboration with management, partners, and clients.
• Experience working in Linux-based infrastructures and with various open-source technologies.
• Awareness of critical concepts in DevOps and Agile principles.
• Experience in configuration management tools (Ansible, Bash Scripting, etc.) is a plus.
• Knowledge of the machine learning life cycle (Planning, Data preparation, Model engineering, Model evaluation, Model deployment, and Monitoring and maintenance).

ABOUT US:

LSEG (London Stock Exchange Group) is more than a diversified global financial markets infrastructure and data business. We are dedicated, open-access partners with a commitment to excellence in delivering the services our customers expect from us. With extensive experience, deep knowledge and worldwide presence across financial markets, we enable businesses and economies around the world to fund innovation, manage risk and create jobs. It’s how we’ve contributed to supporting the financial stability and growth of communities and economies globally for more than 300 years.
Through a comprehensive suite of trusted financial market infrastructure services – and our open-access model – we provide the flexibility, stability and trust that enable our customers to pursue their ambitions with confidence and clarity.
LSEG is headquartered in the United Kingdom, with significant operations in 70 countries across EMEA, North America, Latin America and Asia Pacific. We employ 25,000 people globally, more than half located in Asia Pacific. LSEG’s ticker symbol is LSEG.

• Check deployment pipelines for machine learning models and ensure smooth operation.
• Review code changes and pull requests from the data science team.
• Trigger CI/CD pipelines after code approvals and monitor their execution.
• Ensure all tests pass and model artifacts are generated and stored correctly.
• Deploy updated models to production following successful pipeline completion.
• Collaborate with data scientists and DevOps teams for integration and deployment.
• Containerize models using Docker and deploy them on cloud platforms (AWS)
• Set up monitoring tools (e.g., DataDog, AWS CloudWatch) to track metrics such as response time, error rates, and resource utilization.
• Establish alerts and notifications to detect anomalies or deviations from expected behavior(knowlage on Moogsoft/BigPanda/ServiceNow/ServiceCloud is added advantage).
• Analyze monitoring data, logs, and system metrics to troubleshoot issues.
• Collaborate with the data science team to develop updated pipelines and address any faults.
• Document changes, optimizations, and troubleshooting processes