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- Tasks: Join our GenAI team as an MLOps Engineer, working on innovative AI/ML projects.
- Company: GlobalLogic is a leading tech company focused on cutting-edge technology solutions.
- Benefits: Enjoy flexible work options, competitive salary, and opportunities for professional growth.
- Why this job: Be part of a dynamic team shaping the future of AI with impactful projects.
- Qualifications: Experience in MLOps, cloud platforms, and programming languages like Python required.
- Other info: Ideal for tech enthusiasts eager to tackle complex AI challenges.
The predicted salary is between 36000 - 60000 £ per year.
GlobalLogic are hiring for an MLOps engineer to work on cutting edge projects within our GenAI team. To be successful in the position we require experience in some/all of these areas:
- Proven experience as an MLOps Engineer or in a similar role, with an excellent understanding of AI/ML lifecycle management.
- Strong experience deploying and productionising ML models.
- Familiarity with data engineering concepts, including data pipelines, ETL processes, and big data technologies.
- Excellent problem-solving skills and the ability to troubleshoot complex issues in AI/ML systems.
- Skills with MLOps concepts and principles.
- Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) and containerization tools (e.g., Docker, Kubernetes).
- Proficiency in programming languages such as Python, experience with AI/ML frameworks (e.g., TensorFlow, PyTorch), and experience with MLOps frameworks/tools (e.g. Sagemaker pipelines, Azure ML Studio, VertexAI, Kubeflow, MLFlow, Seldon, EvidentlyAI).
MLOps Engineer employer: GlobalLogic
Contact Detail:
GlobalLogic Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land MLOps Engineer
✨Tip Number 1
Familiarise yourself with the specific MLOps tools and frameworks mentioned in the job description, such as Sagemaker pipelines and Kubeflow. Having hands-on experience or projects showcasing these tools can significantly boost your chances.
✨Tip Number 2
Engage with the MLOps community through forums, webinars, or local meetups. Networking with professionals in the field can provide insights into the latest trends and potentially lead to referrals for positions like the one at GlobalLogic.
✨Tip Number 3
Prepare to discuss real-world scenarios where you've deployed ML models or solved complex issues in AI/ML systems. Being able to articulate your problem-solving process will demonstrate your expertise and fit for the role.
✨Tip Number 4
Showcase your cloud platform experience by discussing any projects you've worked on using AWS, Google Cloud, or Azure. Highlighting your familiarity with these platforms will align well with the requirements of the MLOps Engineer position.
We think you need these skills to ace MLOps Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience as an MLOps Engineer or in similar roles. Emphasise your understanding of the AI/ML lifecycle management and any relevant projects you've worked on.
Showcase Technical Skills: Clearly list your technical skills related to MLOps, including familiarity with cloud platforms like AWS, Google Cloud, or Azure, and containerization tools such as Docker and Kubernetes. Mention your proficiency in programming languages like Python and any AI/ML frameworks you have used.
Demonstrate Problem-Solving Abilities: In your application, provide examples of how you've tackled complex issues in AI/ML systems. This could be through specific projects or challenges you've faced in previous roles.
Craft a Compelling Cover Letter: Write a cover letter that not only summarises your qualifications but also expresses your enthusiasm for working on cutting-edge projects within the GenAI team. Make it personal and connect your experiences to the job description.
How to prepare for a job interview at GlobalLogic
✨Showcase Your MLOps Experience
Be prepared to discuss your previous roles as an MLOps Engineer or in similar positions. Highlight specific projects where you successfully deployed and productionised ML models, and be ready to explain the challenges you faced and how you overcame them.
✨Demonstrate Technical Proficiency
Familiarise yourself with the key technologies mentioned in the job description, such as cloud platforms like AWS, Google Cloud, or Azure, and containerisation tools like Docker and Kubernetes. Be ready to discuss your hands-on experience with these tools and how you've used them in past projects.
✨Problem-Solving Scenarios
Prepare for questions that assess your problem-solving skills. Think of complex issues you've encountered in AI/ML systems and how you approached troubleshooting them. Use the STAR method (Situation, Task, Action, Result) to structure your responses.
✨Familiarity with Data Engineering Concepts
Since data engineering is a crucial part of the role, brush up on concepts like data pipelines, ETL processes, and big data technologies. Be ready to discuss how these concepts integrate with MLOps and provide examples from your experience.
