At a Glance
- Tasks: Design and deploy scalable AI solutions using Azure and Generative AI.
- Company: Join a forward-thinking AI engineering team at iXceed Solutions in Greater London.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Dynamic team environment with exciting projects and career advancement potential.
- Why this job: Be at the forefront of AI innovation and make a real impact in the tech world.
- Qualifications: 5+ years in machine learning, with expertise in MLOps, Docker, and Kubernetes.
The predicted salary is between 60000 - 84000 £ per year.
iXceed Solutions is looking for an experienced ML Engineer to join their AI engineering team in Greater London. The successful candidate will utilize their expertise in Azure, Generative AI, and Large Language Models (LLMs) to design and deploy scalable AI solutions.
The ideal candidate should have at least 5 years of experience in machine learning engineering, with a strong focus on MLOps and model deployment strategies. Familiarity with Docker and Kubernetes is essential for this role.
Azure GenAI/LLM Engineer — RAG, MLOps, K8s employer: ixceed solutions
iXceed Solutions is an exceptional employer that fosters a collaborative and innovative work culture in the heart of Greater London. With a strong emphasis on employee growth, we offer continuous learning opportunities and the chance to work with cutting-edge technologies in AI and machine learning. Our commitment to a supportive environment ensures that every team member can thrive and contribute meaningfully to impactful projects.
StudySmarter Expert Advice🤫
We think this is how you could land Azure GenAI/LLM Engineer — RAG, MLOps, K8s
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving Azure, Generative AI, and LLMs. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for technical interviews by brushing up on MLOps and model deployment strategies. Practice coding challenges and be ready to discuss your experience with Docker and Kubernetes. Confidence is key!
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities that might just be the perfect fit for you. Plus, it’s a great way to get noticed by our hiring team.
We think you need these skills to ace Azure GenAI/LLM Engineer — RAG, MLOps, K8s
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with Azure, Generative AI, and LLMs. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re passionate about AI engineering and how your background in MLOps and Kubernetes makes you a perfect fit for our team.
Showcase Your Projects:If you've worked on any cool projects involving model deployment or scalable AI solutions, make sure to mention them. We love seeing real-world applications of your skills!
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 from our team!
How to prepare for a job interview at ixceed solutions
✨Know Your Tech Inside Out
Make sure you brush up on your knowledge of Azure, Generative AI, and Large Language Models. Be ready to discuss specific projects you've worked on, especially those involving MLOps and model deployment strategies. This will show that you’re not just familiar with the concepts but have practical experience.
✨Showcase Your MLOps Skills
Prepare to talk about your experience with MLOps in detail. Think of examples where you’ve successfully implemented CI/CD pipelines for machine learning models. Highlight any challenges you faced and how you overcame them, as this demonstrates problem-solving skills.
✨Docker and Kubernetes Know-How
Since familiarity with Docker and Kubernetes is essential, be ready to explain how you’ve used these tools in past projects. Discuss how you’ve managed containerisation and orchestration of ML models, as this will be crucial for the role.
✨Ask Insightful Questions
Interviews are a two-way street! Prepare some thoughtful questions about the team’s current projects or the company’s approach to AI solutions. This shows your genuine interest in the role and helps you assess if it’s the right fit for you.
