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- Tasks: Lead the design and delivery of innovative AI/ML solutions that drive organisational success.
- Company: Join a forward-thinking company committed to ethical AI and collaborative culture.
- Benefits: Enjoy competitive salaries, flexible working, generous holiday, and a contributory pension scheme.
- Other info: Flexible working options and a supportive environment for personal and professional growth.
- Why this job: Make a real impact in AI while mentoring future tech leaders and driving innovation.
- Qualifications: Extensive experience in AI/ML solutions, strong Python skills, and leadership capabilities.
The predicted salary is between 70000 - 90000 £ per year.
Lead the design and delivery of end-to-end AI/ML solutions aligned to organisational strategy and operational objectives.
Define scalable, secure, and maintainable AI architectures, covering data pipelines, model lifecycle management, deployment, and monitoring.
Establish architectural standards, design patterns, and engineering best practices.
Provide technical oversight across the full AI lifecycle: concept, prototyping, production deployment, and optimisation.
Review and approve solution designs produced by AI Solution Engineers and wider delivery teams.
Lead, mentor, and develop AI Solution Engineer(s) and associated technical staff.
Set technical direction and ensure consistency in engineering standards across AI initiatives.
Conduct code and design reviews to ensure quality, security, and maintainability.
Identify capability gaps and drive upskilling across the AI team.
Foster a collaborative, high-performance engineering culture.
Own technical delivery of AI solutions, ensuring alignment with scope, timelines, and quality expectations.
Oversee deployment of AI systems into secure or controlled environments.
Ensure compatibility of AI solutions with DevSecOps pipelines and enterprise technology stacks.
Define and monitor performance metrics for deployed models, ensuring operational effectiveness and reliability.
Lead remediation and optimisation efforts when performance, compliance, or operational risks arise.
Embed ethical AI principles, including fairness, explainability, auditability, and transparency, into solution design.
Ensure compliance with data governance, security, and regulatory requirements.
Support accreditation and assurance activities within regulated or defence environments.
Identify, assess, and mitigate technical risks associated with AI deployment.
Translate operational or business requirements into AI-enabled solution approaches.
Engage senior stakeholders, providing clear technical briefings and strategic recommendations.
Contribute to AI capability roadmaps and innovation strategies.
Lead technical input into bids, proposals, or programme planning activities where required.
Conduct horizon scanning to evaluate emerging AI technologies and assess relevance to operational use cases.
Drive continuous improvement of AI engineering practices, tooling, and delivery methods.
Champion adoption of MLOps practices to enhance repeatability, traceability, and scalability.
Promote knowledge sharing and best practice dissemination across engineering teams.
Extensive experience in leading technical teams designing and delivering end-to-end AI/ML solutions from concept to production within regulated or controlled environments.
Strong Python expertise and deep knowledge of ML frameworks.
Experience defining scalable ML architectures, including lifecycle management, monitoring, and resilience strategies.
Experience overseeing deployment using containerisation and orchestration platforms and governing CI/CD and MLOps practices.
Strong understanding of API-led integration within enterprise systems.
Experience embedding responsible AI principles into architectural design.
Strong understanding of secure coding, data protection, and governance frameworks, and experience supporting accreditation or assurance processes.
Accountability for technical risk identification and mitigation.
Experience translating operational requirements into AI-enabled capabilities.
Ability to engage and influence senior stakeholders.
Experience defining AI roadmaps and technical strategy.
Strong decision-making capability balancing innovation, risk, and operational constraints.
Proven experience leading, mentoring, and developing AI/ML engineers.
Strong systems-level analytical thinking.
Ability to operate autonomously with accountability for outcomes.
Degree in relevant discipline or equivalent senior-level experience.
Competitive salaries.
25 days holiday + statutory public holidays, plus opportunity to buy and sell up to 5 days (37hr).
Contributory Pension Scheme (up to 10.5% company contribution).
Company bonus scheme (discretionary).
6 times salary ‘Life Assurance’ with pension.
Flexible Benefits scheme with extensive salary sacrifice schemes, including Health Cashplan, Dental, and Cycle to Work amongst others.
Enhanced sick pay.
Enhanced family friendly policies including enhanced maternity, paternity & shared parental leave.
37hr working week, although hours may vary depending on role, job requirement or site-specific arrangements.
Remote, hybrid and site based working opportunities, dependant on your needs and the requirements of the role.
A grownup flexible working culture that is output, not time spent at desk, focussed. More formal flexible working arrangements can also be requested and assessed subject to the role.
Please enquire or highlight any request to our Talent Acquisition team to explore the flexible working possibilities.
Up to 5 paid days volunteering each year.
AI Solution Lead employer: Prattwhitney
Contact Detail:
Prattwhitney Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Solution Lead
✨Tip Number 1
Network like a pro! Reach out to folks in the AI/ML space on LinkedIn or at industry events. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your AI projects, whether they’re personal or professional. 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 AI/ML questions and scenarios. Practice explaining your thought process clearly, as communication is key when discussing complex solutions with stakeholders.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace AI Solution Lead
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter to highlight your experience with AI/ML solutions. We want to see how your skills align with our needs, so don’t hold back on showcasing relevant projects!
Showcase Your Technical Skills: Since this role is all about leading AI solutions, be sure to emphasise your Python expertise and any experience with ML frameworks. We love seeing specific examples of how you've tackled challenges in the past.
Highlight Leadership Experience: As an AI Solution Lead, you’ll be mentoring and guiding teams. Share your experiences in leading technical teams and how you’ve fostered a collaborative culture. We’re keen to know how you’ve developed others in your previous roles!
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 to do!
How to prepare for a job interview at Prattwhitney
✨Know Your AI Inside Out
Make sure you’re well-versed in the latest AI/ML technologies and frameworks. Brush up on your Python skills and be ready to discuss how you've implemented scalable architectures in past projects. This will show that you can lead the design and delivery of AI solutions effectively.
✨Showcase Your Leadership Skills
Prepare examples of how you've mentored and developed technical teams in previous roles. Highlight your experience in conducting code reviews and establishing engineering best practices. This will demonstrate your ability to foster a high-performance engineering culture.
✨Understand the Business Context
Be ready to translate operational requirements into AI-enabled solutions. Familiarise yourself with the company’s strategic objectives and think about how your technical expertise can align with their goals. This will help you engage senior stakeholders effectively during the interview.
✨Emphasise Ethical AI Principles
Discuss your understanding of ethical AI principles such as fairness, explainability, and transparency. Be prepared to share how you've embedded these principles into your architectural designs in the past. This will show that you prioritise responsible AI deployment.