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- Tasks: Lead AI projects, turning innovative ideas into real-world applications.
- Company: Join a global leader in industrial software with a focus on AI.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic role with a chance to mentor junior engineers and explore cutting-edge technologies.
- Why this job: Be at the forefront of applied AI, making a tangible impact in industry.
- Qualifications: 10+ years in AI/ML, strong leadership skills, and hands-on experience with Python.
The predicted salary is between 80000 - 100000 £ per year.
Wenham Carter is proud to be supporting a global leader in industrial software in the search for a Lead AI Engineer to join a specialist AI Investigation & Incubation team. This is a unique opportunity to work at the intersection of advanced AI research and real-world industrial application, taking emerging concepts and turning them into working, deployable systems that deliver measurable impact.
The organisation operates at global scale across energy, manufacturing, and asset-intensive industries, where reliability, performance, and operational feasibility are as important as innovation.
The role is a hands-on technical leadership position focused on exploring, validating, and prototyping next-generation AI capabilities. You’ll be responsible for taking early-stage AI ideas and rapidly turning them into proof-of-concepts and pilots that can be assessed for real-world deployment.
Key focus areas include:
- Evaluating emerging AI technologies for practical industrial application
- Designing and building prototypes, PoCs, and pilot systems
- Developing, fine-tuning, and benchmarking advanced ML models
- Making architectural decisions around deployment, scalability, and integration
- Assessing solutions across cost, security, reliability, and operational feasibility
- Working closely with cross-functional teams to transition validated work into downstream product paths
- Communicating findings clearly to both technical and executive stakeholders
- Mentoring and supporting junior engineers in the team
Key requirements:
- 10+ years of experience in AI/ML engineering, including at least 4 years in a technical leadership role
- Strong academic background in Computer Science, AI/ML, Engineering, or related field
- Deep hands-on experience with Python and modern ML frameworks (PyTorch, TensorFlow, JAX)
- Proven track record delivering complex AI initiatives with tangible outcomes
- Experience working in industrial software, manufacturing, energy, IoT, or other asset-intensive environments is essential
- Strong experience with foundation models, multi-modal systems, agent-based architectures, and RAG techniques
- Ability to translate AI capabilities into real-world enterprise and industrial use cases
- Excellent communication skills, including engagement with senior stakeholders and cross-functional teams
Why this opportunity? This is a chance to sit at the forefront of applied AI in one of the most complex and important industrial domains globally. You’ll have the freedom to explore emerging technologies, but also the responsibility to turn them into something real, scalable, and valuable.
If you’re excited by building at the edge of AI - and seeing your work move beyond the lab into production environments - this is a rare opportunity to do exactly that.
Lead AI Engineer employer: Searches @ Wenham Carter
Contact Detail:
Searches @ Wenham Carter Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead AI Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with professionals on LinkedIn. We can’t stress enough how important it is to build relationships that could lead to job opportunities.
✨Tip Number 2
Showcase your skills! Create a portfolio or GitHub repository with your AI projects. This gives potential employers a tangible look at what you can do, and we all know actions speak louder than words.
✨Tip Number 3
Prepare for interviews by practising common technical questions and scenarios related to AI/ML. We recommend doing mock interviews with friends or using online platforms to get comfortable with the format.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Lead AI Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Lead AI Engineer role. Highlight your hands-on experience with Python and ML frameworks, as well as any leadership roles you've held. We want to see how you can bring your unique expertise to our team!
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 your background makes you a perfect fit for this position. Don’t forget to mention specific projects or achievements that demonstrate your ability to turn ideas into real-world applications.
Showcase Your Projects: If you've worked on any relevant AI projects, make sure to include them in your application. Whether it's prototypes, PoCs, or complex AI initiatives, we want to see tangible outcomes. This will help us understand your practical experience and how you approach problem-solving.
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 shows us you’re serious about joining our team at StudySmarter!
How to prepare for a job interview at Searches @ Wenham Carter
✨Know Your AI Stuff
Make sure you brush up on the latest AI technologies and frameworks like PyTorch and TensorFlow. Be ready to discuss your hands-on experience and how you've applied these tools in real-world scenarios, especially in industrial settings.
✨Showcase Your Leadership Skills
Since this is a technical leadership role, prepare examples of how you've led teams or projects in the past. Highlight your mentoring experiences and how you've helped junior engineers grow, as well as any architectural decisions you've made that had a significant impact.
✨Communicate Clearly
Practice explaining complex AI concepts in simple terms. You’ll need to communicate findings to both technical and executive stakeholders, so being able to articulate your ideas clearly is crucial. Consider doing mock interviews with friends to refine your communication style.
✨Prepare for Real-World Applications
Think about how you can translate AI capabilities into practical solutions for industries like energy and manufacturing. Be ready to discuss specific use cases and how your previous work has delivered measurable impact in similar environments.