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For Companies At a Glance
- Tasks: Manage and optimise ML model deployments for reliability and efficiency.
- Company: Join a leading Scottish company innovating in AI solutions.
- Benefits: Enjoy a hybrid work model and a competitive salary up to £70,000.
- Why this job: Be at the forefront of AI technology and collaborate with talented teams.
- Qualifications: 4+ years in MLOps or software engineering, strong Python skills required.
- Other info: Work with cutting-edge tools like AWS, Docker, and Kubernetes.
The predicted salary is between 42000 - 84000 £ per year.
We are looking for a Senior MLOps Engineer to join a Scottish company working on cutting edge AI solutions. You will play a pivotal role in ensuring that ML initiatives drive value effectively while maintaining operational excellence.
The Role:
- Managing and optimising existing ML model deployments to ensure reliability and efficiency.
- Continuously improving the architecture, processes, and tools used for model deployment, monitoring, and lifecycle management.
- Collaborating closely with data scientists to understand and implement model requirements.
- Partnering with R&D teams to align technical strategies and integrate ML solutions into broader systems.
- Implementing robust CI/CD pipelines, monitoring systems, and infrastructure automation.
- Upholding best practices in security, cost management, and infrastructure design for cloud environments.
The Ideal Candidate:
- 4+ years of experience in MLOps, DevOps, or software engineering roles.
- Strong programming skills in Python and familiarity with ML frameworks.
- Extensive experience with AWS services (e.g., SageMaker, ECS, Lambda) and cloud environments.
- Proficiency with containerization and orchestration tools (Docker, Kubernetes).
Senior MLOps Engineer employer: Resourcing Group
Contact Detail:
Resourcing Group Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior MLOps Engineer
✨Tip Number 1
Network with professionals in the MLOps field, especially those who work with AI solutions. Attend industry meetups or webinars to connect with potential colleagues and learn about the latest trends and technologies.
✨Tip Number 2
Showcase your hands-on experience with AWS services and MLOps tools by contributing to open-source projects or creating your own projects. This practical experience can set you apart from other candidates.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python programming skills and familiarising yourself with ML frameworks. Practice coding challenges that focus on MLOps scenarios to demonstrate your problem-solving abilities.
✨Tip Number 4
Research the company’s current ML initiatives and be ready to discuss how your skills can contribute to their goals. Tailoring your conversation to their specific projects shows genuine interest and can make a strong impression.
We think you need these skills to ace Senior MLOps Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in MLOps, DevOps, or software engineering. Emphasise your programming skills in Python and any familiarity with ML frameworks, as well as your experience with AWS services.
Craft a Compelling Cover Letter: Write a cover letter that specifically addresses the job description. Mention your experience with CI/CD pipelines, containerization, and orchestration tools like Docker and Kubernetes. Show how your skills align with the company's needs.
Showcase Relevant Projects: If you have worked on projects related to ML model deployments or cloud infrastructure, include them in your application. Describe your role and the impact of your contributions to demonstrate your hands-on experience.
Proofread and Edit: Before submitting your application, carefully proofread your documents for any spelling or grammatical errors. A polished application reflects your attention to detail and professionalism.
How to prepare for a job interview at Resourcing Group
✨Showcase Your Technical Skills
Be prepared to discuss your experience with MLOps, DevOps, and software engineering. Highlight specific projects where you've managed ML model deployments and optimised processes, especially using Python and AWS services.
✨Demonstrate Collaboration
Since the role involves working closely with data scientists and R&D teams, be ready to share examples of how you've successfully collaborated in the past. Discuss how you’ve aligned technical strategies and integrated ML solutions into broader systems.
✨Discuss CI/CD and Automation
Make sure to talk about your experience with implementing CI/CD pipelines and infrastructure automation. Provide concrete examples of how you've improved deployment processes and monitoring systems in previous roles.
✨Emphasise Best Practices
Talk about your commitment to best practices in security, cost management, and infrastructure design. Be prepared to discuss how you've upheld these principles in cloud environments, particularly with AWS.
