At a Glance
- Tasks: Design and deploy cutting-edge AI/ML solutions using Azure and Generative AI technologies.
- Company: Join a high-performing AI engineering team at a leading tech company.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic environment with a focus on Responsible AI practices.
- Why this job: Make an impact in the AI field while working with innovative technologies.
- Qualifications: 5+ years in ML Engineering, strong Azure and LLM experience required.
The predicted salary is between 70000 - 90000 £ per year.
We are seeking an experienced ML Engineer with strong expertise in Azure, Generative AI, and Large Language Models (LLMs) to join a high-performing AI engineering team delivering enterprise-scale intelligent solutions. The ideal candidate will have hands-on experience in designing, deploying, and optimizing AI/ML systems, with particular focus on GenAI applications, RAG architectures, model lifecycle management, and scalable MLOps practices.
Key Responsibilities
- Design, develop, and deploy scalable AI/ML solutions using Azure cloud technologies
- Build and optimize LLM-based applications and Generative AI solutions
- Develop Retrieval-Augmented Generation (RAG) pipelines integrating vector databases and enterprise data sources
- Fine-tune pretrained LLMs using PEFT methodologies including LoRA and QLoRA
- Design and maintain robust ETL/ELT data pipelines for AI model training and inference
- Implement AI model monitoring, performance tuning, versioning, and lifecycle management
- Build and manage automated CI/CD pipelines for model deployment and retraining workflows
- Collaborate closely with Data Scientists, DevOps Engineers, and business stakeholders during the end-to-end model development lifecycle
- Deploy containerized AI applications using Docker and Kubernetes
- Ensure AI solutions comply with Responsible AI principles including fairness, transparency, governance, and security standards
- Support infrastructure provisioning and optimization across cloud-based AI environments
- Maintain technical documentation and contribute to best practices for scalable AI engineering
Required Skills and Experience
- 5+ years of experience in Machine Learning Engineering or AI Engineering
- Strong hands-on experience with Microsoft Azure
- Proven experience working with Large Language Models (LLMs) and Generative AI solutions
- Experience building and deploying RAG architectures
- Expertise in MLOps, CI/CD pipelines, and model deployment strategies
- Experience with Docker and Kubernetes
- Strong Python programming skills
- Experience with model monitoring, observability, and performance optimization
- Familiarity with vector databases and embedding workflows
- Strong understanding of AI governance and Responsible AI practices
Nice to Have
- Experience within the Insurance domain
- Exposure to Agentic AI systems and autonomous AI workflows
ML Engineer – GenAI / LLM / Azure employer: ixceed solutions
Join a forward-thinking company that prioritises innovation and employee development, offering a collaborative work culture where your expertise in Azure and Generative AI will be valued. With a strong focus on career growth, you will have access to cutting-edge projects and the opportunity to work alongside industry leaders in a dynamic environment that champions responsible AI practices. Located in a vibrant tech hub, this role not only promises meaningful work but also a chance to thrive in a community that fosters creativity and excellence.
StudySmarter Expert Advice🤫
We think this is how you could land ML Engineer – GenAI / LLM / Azure
✨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, LLMs, and Generative AI. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common ML engineering questions and practical scenarios. Practice explaining your past projects and how you tackled challenges, especially around MLOps and model deployment.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented ML Engineers. Plus, it’s a great way to ensure your application gets seen by the right people.
We think you need these skills to ace ML Engineer – GenAI / LLM / Azure
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 or achievements!
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 makes you a perfect fit for our team. Keep it engaging and personal – we love to see your personality!
Showcase Your Projects:If you've worked on any cool AI/ML projects, make sure to mention them! Whether it's building scalable solutions or optimising models, we want to know what you've done and how it relates to the role.
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’re considered for the role. Plus, it’s super easy – just a few clicks and you’re done!
How to prepare for a job interview at ixceed solutions
✨Know Your Tech Inside Out
Make sure you’re well-versed in Azure, Generative AI, and LLMs. Brush up on your hands-on experience with these technologies, as you’ll likely be asked to discuss specific projects or challenges you've faced. Be ready to explain how you’ve designed, deployed, and optimised AI/ML systems.
✨Showcase Your MLOps Skills
Since MLOps is a key part of the role, prepare to talk about your experience with CI/CD pipelines and model deployment strategies. Have examples ready that demonstrate how you’ve built and managed automated workflows, and don’t forget to mention any tools like Docker and Kubernetes you’ve used.
✨Demonstrate Collaboration
This role involves working closely with Data Scientists and DevOps Engineers, so be prepared to discuss how you’ve collaborated in past projects. Highlight your communication skills and how you’ve contributed to the end-to-end model development lifecycle, ensuring everyone is on the same page.
✨Understand Responsible AI Principles
Familiarise yourself with Responsible AI practices, including fairness, transparency, and governance. Be ready to discuss how you’ve ensured compliance with these principles in your previous work, as this will show that you take ethical considerations seriously in AI development.
