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- Tasks: Join us as a DevOps/MLOps Engineer to deploy and manage machine learning models.
- Company: Be part of a dynamic team driving innovation in machine learning and cloud infrastructure.
- Benefits: Enjoy flexible work options, collaborative culture, and opportunities for growth.
- Why this job: Work with cutting-edge technology and collaborate with talented data scientists and engineers.
- Qualifications: Must have experience in DevOps, coding in Python/Java, and deploying on AWS.
- Other info: Familiarity with CI/CD pipelines and machine learning life cycle is a plus.
The predicted salary is between 36000 - 60000 £ per year.
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 (knowledge on Moogsoft/BigPanda/ServiceNow/ServiceCloud is an 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).
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DevOps/MLOps Engineer employer: London Stock Exchange Group
Contact Detail:
London Stock Exchange Group Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land DevOps/MLOps Engineer
✨Tip Number 1
Familiarize yourself with the specific cloud platforms mentioned in the job description, especially AWS. Hands-on experience with AWS services will not only boost your confidence but also demonstrate your readiness to hit the ground running.
✨Tip Number 2
Brush up on your CI/CD pipeline skills, particularly with tools like Terraform and Ansible. Being able to discuss your past experiences with these tools during the interview can set you apart from other candidates.
✨Tip Number 3
Prepare to showcase your problem-solving abilities by discussing specific challenges you've faced in previous roles. Highlight how you used monitoring tools like AWS CloudWatch to troubleshoot issues effectively.
✨Tip Number 4
Since collaboration is key in this role, think of examples where you've successfully worked with data scientists or cross-functional teams. Being able to communicate your collaborative experiences will resonate well with the hiring team.
We think you need these skills to ace DevOps/MLOps Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in DevOps and MLOps. Focus on your proficiency with cloud platforms, CI/CD pipelines, and any machine learning frameworks you've worked with.
Craft a Strong Cover Letter: In your cover letter, emphasize your collaboration skills and how you can work closely with data scientists and DevOps teams. Mention specific projects where you've successfully integrated machine learning models.
Showcase Technical Skills: Clearly list your technical skills related to the job description, such as coding in Python and Java, using Terraform or Ansible, and experience with AWS. Provide examples of how you've applied these skills in past roles.
Highlight Problem-Solving Abilities: Include examples of how you've tackled challenges in previous positions, especially those related to deployment pipelines, monitoring tools, or troubleshooting issues. This will demonstrate your strong problem-solving approach.
How to prepare for a job interview at London Stock Exchange Group
✨Showcase Your Technical Skills
Be prepared to discuss your experience with cloud platforms, CI/CD pipelines, and machine learning frameworks. Highlight specific projects where you successfully deployed models or managed infrastructure, as this will demonstrate your hands-on expertise.
✨Collaborate Effectively
Since the role involves working closely with data scientists and DevOps teams, emphasize your communication skills and ability to collaborate. Share examples of how you've worked in cross-functional teams to achieve project goals.
✨Demonstrate Problem-Solving Abilities
Prepare to discuss challenges you've faced in previous roles, particularly related to deployment and monitoring. Explain your troubleshooting process and how you resolved issues, showcasing your analytical thinking.
✨Familiarize Yourself with Monitoring Tools
Knowledge of tools like DataDog and AWS CloudWatch is crucial. Be ready to talk about how you've used these tools to track metrics and set up alerts. This will show that you understand the importance of monitoring in maintaining model performance.
