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- Tasks: Set up Redis clusters and develop real-time streaming pipelines using Kafka/Flink.
- Company: Dynamic tech solutions company based in London with a hybrid work model.
- Benefits: Competitive pay, flexible working, and potential for contract extension.
- Why this job: Join a fast-paced team and work on cutting-edge ML projects that make a difference.
- Qualifications: Experience with SageMaker MLOps, Pytorch, and data pipeline development.
- Other info: Perfect for those who thrive in dynamic environments and love tackling challenges.
The predicted salary is between 42000 - 84000 £ per year.
A tech solutions company in London is looking for an experienced ML Engineer to work in a hybrid model. The role involves:
- Setting up Redis clusters
- Developing Kafka/Flink streaming pipelines
- Implementing S3 data pipelines
Candidates should have expertise in:
- SageMaker MLOps
- Training and model deployment
- Experience with Pytorch
This position offers a duration of 5 months with a possibility of extension and is perfect for those who thrive in dynamic environments.
ML Engineer: Real-Time Pipelines & SageMaker (London, Hybrid) employer: Kryptos Technologies limited
Contact Detail:
Kryptos Technologies limited Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Engineer: Real-Time Pipelines & SageMaker (London, Hybrid)
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can land you that ML Engineer gig.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects with Redis, Kafka, and SageMaker. We want to see your hands-on experience, so don’t be shy about sharing your work on GitHub or personal websites.
✨Tip Number 3
Prepare for those interviews! Brush up on your knowledge of real-time data pipelines and MLOps. We suggest practising common interview questions and even doing mock interviews with friends to boost your confidence.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. We’re always on the lookout for talented ML Engineers, so don’t miss out on the chance to join us!
We think you need these skills to ace ML Engineer: Real-Time Pipelines & SageMaker (London, Hybrid)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Redis clusters, Kafka/Flink streaming pipelines, and SageMaker. We want to see how your skills align with the role, so don’t be shy about showcasing your expertise!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re the perfect fit for this ML Engineer role. Share specific examples of your work with MLOps and Pytorch that demonstrate your ability to thrive in dynamic environments.
Showcase Your Projects: If you've worked on relevant projects, make sure to mention them! We love seeing real-world applications of your skills, especially those involving S3 data pipelines or model deployment. It gives us a better idea of what you can bring to the table.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates. Plus, we love seeing candidates who take that extra step!
How to prepare for a job interview at Kryptos Technologies limited
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technologies mentioned in the job description, like Redis, Kafka, and SageMaker. Brush up on your knowledge of MLOps and be ready to discuss how you've implemented these tools in past projects.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced while developing real-time pipelines or deploying models. Use the STAR method (Situation, Task, Action, Result) to structure your answers and highlight your problem-solving abilities.
✨Demonstrate Your Adaptability
Since this role is in a dynamic environment, be ready to share examples of how you've adapted to changing requirements or technologies in previous roles. This will show that you can thrive in a fast-paced setting.
✨Ask Insightful Questions
Prepare thoughtful questions about the team’s current projects or the company’s approach to machine learning. This not only shows your interest but also helps you gauge if the company culture aligns with your values.
