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- Tasks: Design and deliver next-gen AI solutions for public sector and enterprise clients.
- Company: Join a forward-thinking tech company at the forefront of AI innovation.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Why this job: Make a real impact by leveraging cutting-edge AI technologies to transform businesses.
- Qualifications: 3+ years in machine learning or AI, with skills in GenAI and LLMs.
- Other info: Dynamic team environment with a focus on collaboration and innovation.
The predicted salary is between 36000 - 60000 £ per year.
We’re hiring an AI & Machine Learning Engineer to help design and deliver next‑generation AI solutions across our public sector and enterprise clients. You’ll work across Machine Learning, Data Engineering, and advanced AI technologies including Generative AI, Agentic AI, and Large Language Models as part of our growing AI Engineering team.
You will collaborate with data architects, engineers, and business stakeholders to create innovative, cloud-based AI solutions that leverage the latest advancements in GenAI. You will help clients unlock new value from their data, automate complex processes, and drive digital transformation through the practical application of cutting‑edge AI.
Responsibilities- Deploy, fine‑tune and monitor Generative AI models and Agentic AI for enterprise use cases.
- Develop and implement Retrieval‑Augmented Generation (RAG) pipelines and advanced context engineering strategies.
- Integrate Agentic AI into business workflows.
- Collaborate with data engineers to bring Agentic capabilities to production.
- Stay current with AI trends, tools, and best practices, and drive innovation within the team.
- 3+ years of experience in machine learning, AI, data science, or software development, with recent focus on GenAI and LLMs.
- Experience with GenAI frameworks (e.g. Azure Foundry, CrewAI and Hugging Face).
- Proficient in context engineering, RAG, and LLMOps.
- Experience deploying ML/AI solutions on Azure (Azure OpenAI, Azure AI Foundry, Azure ML Studio).
- Experience with Azure data and analytics services (Data Factory, Data Lake, Synapse Analytics, SQL Database).
- Programming skills in Python, R, or similar languages.
- Familiarity with ML frameworks and libraries (TensorFlow, PyTorch, Scikit‑learn).
- Experience with Azure DevOps, GitHub, or similar tools.
- Experience in Computer Vision for Optical Character Recognition (OCR) and object recognition.
- Knowledge and experience of the following would be advantageous: Hands‑on experience with MLflow, Databricks CLI, Terraform, automated retraining pipelines, drift detection, MLSecOps, access control, audit logging, and documentation.
AI & Machine learning Engineer in Reading employer: F
Contact Detail:
F Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI & Machine learning Engineer in Reading
✨Tip Number 1
Network like a pro! Reach out to your connections in the AI and machine learning space. Attend meetups, webinars, or conferences where you can chat with industry folks. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving Generative AI and LLMs. Share it on platforms like GitHub or your personal website. This gives potential employers a taste of what you can do!
✨Tip Number 3
Prepare for interviews by brushing up on the latest AI trends and tools. Be ready to discuss your experience with Azure and ML frameworks. Practise common interview questions related to machine learning and data engineering to boost your confidence.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Keep an eye on our job listings and make sure your application stands out by tailoring it to the role you're after.
We think you need these skills to ace AI & Machine learning Engineer in Reading
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with AI and machine learning, especially focusing on 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 and how you can contribute to our team. We love seeing enthusiasm and a clear understanding of the role, so let your personality come through.
Showcase Your Projects: If you've worked on any cool AI projects, make sure to mention them! Whether it's deploying models or developing pipelines, we want to know what you've done. Links to GitHub or personal projects can really make your application stand out.
Apply Through Our Website: We encourage you to apply directly through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to keep track of your application status. Plus, we love seeing candidates who take that extra step!
How to prepare for a job interview at F
✨Know Your AI Stuff
Make sure you brush up on the latest trends in AI and machine learning, especially around Generative AI and Large Language Models. Be ready to discuss your experience with specific frameworks like Azure Foundry or Hugging Face, and how you've applied them in real-world scenarios.
✨Showcase Your Projects
Prepare to talk about your past projects that involved deploying ML solutions. Highlight any experience with context engineering or RAG pipelines, and be specific about your role in those projects. This will show your practical knowledge and problem-solving skills.
✨Collaboration is Key
Since this role involves working with data architects and engineers, be ready to discuss how you've collaborated in the past. Share examples of how you integrated AI into business workflows and the impact it had on the team or project outcomes.
✨Stay Current and Curious
Demonstrate your passion for AI by discussing recent advancements or tools you've been exploring. Mention any online courses, workshops, or communities you're part of. This shows that you're proactive and committed to continuous learning in a fast-evolving field.
