AI Models Lead in Birmingham

AI Models Lead in Birmingham

Birmingham Full-Time 100000 - 120000 € / year (est.) Home office (partial)
Experis IT

At a Glance

  • Tasks: Lead the development and fine-tuning of cutting-edge AI models for real-world applications.
  • Company: Join a rapidly growing software organisation at the forefront of AI innovation.
  • Benefits: Competitive salary up to £120,000, bonuses, and comprehensive benefits package.
  • Other info: Flexible hybrid working with opportunities for significant career growth.
  • Why this job: Shape the future of AI by building foundational capabilities from the ground up.
  • Qualifications: Experience in AI model development and a passion for innovative technology.

The predicted salary is between 100000 - 120000 € per year.

Hybrid: Midlands-based 2-3 days in the office or remote contracts for candidates outside of 35 miles

Paying up to £120,000 + bonus + Benefits

Permanent

Experis are delighted to be partnering with a highly successful and growing software organisation as they invest heavily in building a cutting-edge AI capability at the heart of their product suite. We are supporting them in the search for an AI Models Lead, a key foundational hire who will own the development, fine-tuning, and production delivery of domain-specific AI models within a large-scale programme. This is a rare opportunity to join at an early stage and build a fine-tuning capability from first principles, shaping how AI models are developed, evaluated, and deployed in real-world, regulated environments.

What You'll Be Doing

  • Designing and leading the end-to-end model fine-tuning strategy, including SFT, LoRA/QLoRA, and optimisation approaches
  • Selecting and evaluating base models (eg Mistral, Qwen, PHI, Falcon) based on performance, cost, and use case
  • Defining evaluation frameworks and standards to ensure models meet production-grade quality and reliability
  • Building and scaling a suite of fine-tuned models to support multiple product use cases
  • Owning experiment design and reproducibility, including tracking, benchmarking, and iteration cycles

AI Models Lead in Birmingham employer: Experis IT

Join a forward-thinking software organisation that prioritises innovation and employee development, offering a hybrid work model in the Midlands. With competitive salaries, bonuses, and a strong focus on building cutting-edge AI capabilities, this company fosters a collaborative culture where your expertise will directly influence the future of AI in real-world applications. Enjoy unique growth opportunities as you lead the charge in developing domain-specific AI models, all while being part of a supportive team dedicated to excellence.

Experis IT

Contact Detail:

Experis IT Recruiting Team

StudySmarter Expert Advice🤫

We think this is how you could land AI Models Lead in Birmingham

Tip Number 1

Network like a pro! Reach out to folks in the AI space on LinkedIn or at industry events. We all know that sometimes it’s not just what you know, but who you know that can get you in the door.

Tip Number 2

Show off your skills! Create a portfolio showcasing your AI projects and models. We want to see your work in action, so make it easy for potential employers to see what you can bring to the table.

Tip Number 3

Prepare for those interviews! Research the company and their AI initiatives. We suggest practising common interview questions related to model fine-tuning and optimisation strategies to really impress them.

Tip Number 4

Apply through our website! We’ve got loads of opportunities waiting for you, and applying directly can sometimes give you an edge. Let’s get you that AI Models Lead position!

We think you need these skills to ace AI Models Lead in Birmingham

AI Model Development
Fine-Tuning Strategies
SFT (Supervised Fine-Tuning)
LoRA/QLoRA
Model Optimisation
Base Model Evaluation
Performance Analysis

Some tips for your application 🫡

Show Your Passion for AI:When you're writing your application, let your enthusiasm for AI shine through! We want to see how excited you are about the potential of AI models and how you can contribute to our cutting-edge projects.

Tailor Your Experience:Make sure to highlight your relevant experience in model fine-tuning and evaluation. We’re looking for someone who can lead the charge, so be specific about your past roles and achievements that align with what we do.

Be Clear and Concise:Keep your application straightforward and to the point. We appreciate clarity, so avoid jargon unless it’s necessary. Make it easy for us to see why you’re the perfect fit for the AI Models Lead role!

Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for this exciting opportunity. We can’t wait to hear from you!

How to prepare for a job interview at Experis IT

Know Your AI Models Inside Out

Make sure you’re well-versed in the latest AI models and fine-tuning strategies. Brush up on SFT, LoRA/QLoRA, and optimisation approaches, as these will likely come up during your interview. Being able to discuss specific models like Mistral or Falcon and their applications will show your expertise.

Demonstrate Your Problem-Solving Skills

Prepare to share examples of how you've tackled challenges in model development or deployment. Think about times when you had to design experiments or ensure reproducibility. This will highlight your hands-on experience and ability to think critically under pressure.

Showcase Your Leadership Qualities

As an AI Models Lead, you’ll need to demonstrate your leadership skills. Be ready to discuss how you’ve led projects or teams in the past, particularly in developing and scaling AI models. Highlight your ability to mentor others and drive a project from concept to production.

Ask Insightful Questions

Prepare thoughtful questions about the company’s AI strategy and future projects. This not only shows your interest but also helps you gauge if the company aligns with your career goals. Inquire about their evaluation frameworks and how they measure success in model performance.