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For Companies At a Glance
- Tasks: Lead the development of innovative AI solutions and support engineering teams in adopting AI.
- Company: Join a forward-thinking company at the forefront of AI technology in the UK.
- Benefits: Enjoy a competitive salary, bonus, and hybrid working model with flexibility.
- Why this job: Be part of a dynamic team shaping the future of AI while mentoring junior engineers.
- Qualifications: Experience in Generative AI, strong coding skills in TypeScript and Python, and MLOps knowledge required.
- Other info: Opportunity to work with cutting-edge technologies and make a real impact in the industry.
The predicted salary is between 48000 - 64000 £ per year.
We are looking for a Lead AI Engineer to define and deliver the next phase of AI for our client. This role combines deep expertise in GenAI with strong hands-on experience in classical machine learning. You will play a key role in building internal AI tools, integrating scalable ML systems, and shaping how AI is used across the business.
Responsibilities
- Lead the development of GenAI solutions including LLMs, embeddings, and retrieval-based systems
- Design, build, and support internal platforms that help engineering teams adopt AI effectively
- Guide the adoption of classical machine learning models that address practical business problems
- Write high-quality, maintainable code in TypeScript and Python
- Drive excellence in MLOps practices including model deployment, monitoring, and governance
- Collaborate with engineers, product owners, and security teams to ensure solutions are secure and reliable
- Coach and mentor junior engineers and contribute to the overall strategy of the AI platform
What You Will Need
- Hands-on experience with Generative AI technologies such as LLMs, prompt engineering, and vector-based search
- Strong foundation in traditional ML techniques such as regression, classification, and clustering
- Proficiency in both TypeScript and Python for building robust applications and tools
- Knowledge of MLOps practices, CI/CD pipelines, and software delivery best practices
- Experience deploying ML models into production environments using cloud platforms such as Azure or GCP
- Ability to work in agile teams and communicate effectively with technical and non-technical stakeholders
Bonuses
- Experience with tools such as Terraform, Ansible, Packer, or ML platforms like Vertex AI and Azure ML
- Familiarity with cloud AI services such as Azure Vision or Google Vision AI
- Understanding of modern application architecture, including API-first design and service-oriented patterns
- Background in AI safety, model evaluation, or secure model deployment
This role is for someone who can combine strong software engineering skills with deep AI knowledge to build products that drive impact. If that sounds like you, we would love to hear from you.
Lead AI Engineer - GenAI and ML (, , United Kingdom) employer: middle
Contact Detail:
middle Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead AI Engineer - GenAI and ML (, , United Kingdom)
✨Tip Number 1
Network with professionals in the AI and machine learning fields. Attend industry meetups, webinars, or conferences to connect with potential colleagues and learn about the latest trends in Generative AI. This can help you gain insights into what companies like us are looking for in candidates.
✨Tip Number 2
Showcase your hands-on experience with Generative AI technologies by working on personal projects or contributing to open-source initiatives. This not only demonstrates your skills but also gives you practical examples to discuss during interviews, making you a more attractive candidate.
✨Tip Number 3
Familiarise yourself with MLOps practices and cloud platforms like Azure or GCP. Consider obtaining relevant certifications or completing online courses that focus on deploying ML models in production environments. This knowledge will set you apart from other applicants.
✨Tip Number 4
Prepare to discuss how you've collaborated with cross-functional teams in previous roles. Being able to communicate effectively with both technical and non-technical stakeholders is crucial for this position, so have examples ready that highlight your teamwork and leadership skills.
We think you need these skills to ace Lead AI Engineer - GenAI and ML (, , United Kingdom)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Generative AI technologies, classical machine learning techniques, and proficiency in TypeScript and Python. Use specific examples that demonstrate your hands-on experience and achievements in these areas.
Craft a Compelling Cover Letter: In your cover letter, express your passion for AI and how your skills align with the responsibilities of the Lead AI Engineer role. Mention your experience with MLOps practices and your ability to mentor junior engineers, as these are key aspects of the position.
Showcase Relevant Projects: If you have worked on projects involving LLMs, prompt engineering, or deploying ML models in production, be sure to include these in your application. Highlight your role in these projects and the impact they had on the business.
Prepare for Technical Questions: Anticipate technical questions related to AI, machine learning, and software engineering during the interview process. Brush up on your knowledge of CI/CD pipelines, cloud platforms like Azure or GCP, and modern application architecture to demonstrate your expertise.
How to prepare for a job interview at middle
✨Showcase Your Technical Expertise
Be prepared to discuss your hands-on experience with Generative AI technologies and classical machine learning techniques. Highlight specific projects where you've successfully implemented LLMs, embeddings, or other relevant systems.
✨Demonstrate Coding Proficiency
Since the role requires writing high-quality code in TypeScript and Python, be ready to showcase your coding skills. You might be asked to solve a coding challenge or explain your approach to building robust applications.
✨Discuss MLOps Practices
Familiarise yourself with MLOps practices, CI/CD pipelines, and model deployment strategies. Be prepared to discuss how you have applied these practices in previous roles, especially in cloud environments like Azure or GCP.
✨Emphasise Collaboration Skills
This role involves working closely with engineers, product owners, and security teams. Be ready to share examples of how you've effectively communicated and collaborated with both technical and non-technical stakeholders in past projects.
