At a Glance
- Tasks: Build and deploy intelligent systems for real-world business applications using AI.
- Company: Join a forward-thinking tech company focused on enterprise AI solutions.
- Benefits: Competitive salary, remote work flexibility, and opportunities for professional growth.
- Other info: Collaborative environment with high ownership and career advancement potential.
- Why this job: Make a real impact by shaping AI systems in regulated industries.
- Qualifications: 5+ years in data science and machine learning with strong Python skills.
The predicted salary is between 60000 - 80000 £ per year.
We’re hiring a Digital Intelligence Engineer to help build, deploy, and evaluate intelligent systems for enterprise clients across regulated industries. This is a hybrid role for someone who is data science first and systems engineering second — combining strong traditional machine learning expertise with the ability to deploy production-grade AI systems and communicate results directly to clients.
The Role:
- Build and deploy intelligent systems for real-world business applications
- Apply statistical analysis, classical machine learning, and experiment design to enterprise problems
- Design and evaluate ML systems using robust performance metrics and validation approaches
- Work across the full lifecycle from modelling through to deployment and scaling
- Lead client conversations around AI evaluation, performance, and system behaviour
- Present data-backed findings and explain technical outcomes to enterprise stakeholders
- Collaborate closely with engineering teams to integrate AI systems into production environments
- Contribute to internal knowledge sharing and AI engineering best practices
What We’re Looking For:
- 5+ years of experience across data science and machine learning with strong software engineering capability
- Strong expertise in traditional ML, statistical analysis, evaluation methods, and experiment design
- Experience building, deploying, and scaling ML systems in production
- Strong Python and applied machine learning experience
- Ability to communicate complex AI concepts clearly to enterprise clients
- Experience delivering real-world business projects using production AI systems
- Comfortable working independently and contributing to technical direction
Nice to Have:
- Experience in regulated industries such as pharma, biopharma, medtech, or financial services
- GAMP experience with ML systems in production
- Experience with LLMs and practical AI applications across business use cases
- Experience with cloud infrastructure, APIs, or ML deployment tooling
Why Join:
- Work on real-world enterprise AI deployments
- High ownership across AI delivery, evaluation, and deployment
- Collaborative, engineering-led environment
- Opportunity to shape how intelligent systems are deployed in regulated industries
- Build AI systems that solve practical business problems in production
Machine Learning Engineer in Warrington employer: Immersum
Join us as a Digital Intelligence Engineer and be part of a forward-thinking team that values innovation and collaboration. Our hybrid work culture allows for flexibility while providing opportunities for meaningful engagement with enterprise clients in regulated industries. With a strong focus on employee growth, you will have the chance to lead impactful AI projects, enhance your skills in a supportive environment, and contribute to shaping the future of intelligent systems.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer in Warrington
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We can’t stress enough how important it is to make connections; you never know who might have the inside scoop on job openings.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects. We recommend including real-world applications and results, as this will help you stand out when chatting with potential employers.
✨Tip Number 3
Prepare for those interviews! Brush up on your technical knowledge and be ready to discuss your experience with Python and ML systems. We suggest practising common interview questions and even doing mock interviews with friends.
✨Tip Number 4
Apply through our website! We’re always on the lookout for talented individuals like you. By applying directly, you’ll ensure your application gets the attention it deserves, and you might just land that dream role with us!
We think you need these skills to ace Machine Learning Engineer in Warrington
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with Python, machine learning, and statistical modelling. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or achievements!
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 our Digital Intelligence Engineer role. Keep it engaging and personal!
Showcase Your Communication Skills:Since you'll be leading client conversations, it's crucial to demonstrate your ability to communicate complex ideas clearly. Include examples in your application where you've successfully explained technical concepts to non-technical stakeholders.
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 don’t miss out on any important updates from our team!
How to prepare for a job interview at Immersum
✨Know Your Tech Stack Inside Out
Make sure you’re well-versed in Python, machine learning, and statistical modelling. Brush up on your knowledge of LLMs and production ML systems, as these are crucial for the role. Be ready to discuss specific projects where you've applied these technologies.
✨Prepare for Client Conversations
Since this role involves leading discussions with clients about AI evaluation and performance, practice explaining complex concepts in simple terms. Think of examples where you’ve successfully communicated technical outcomes to non-technical stakeholders.
✨Showcase Your Problem-Solving Skills
Be prepared to discuss how you’ve tackled real-world business problems using machine learning. Highlight your experience with experiment design and robust performance metrics, and be ready to share specific results from your past projects.
✨Demonstrate Collaboration and Independence
This role requires both teamwork and the ability to work independently. Share examples of how you’ve collaborated with engineering teams to integrate AI systems, as well as instances where you’ve taken the lead on projects or contributed to technical direction.
