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- Tasks: Transform complex behavioural data into intelligent systems and deploy machine learning models.
- Company: Join a vibrant early tech start-up in Manchester focused on AI and behavioural biometrics.
- Benefits: Enjoy hybrid working, competitive salary, and potential equity consideration.
- Why this job: Be part of an innovative team shaping the future of AI and make a real impact.
- Qualifications: 2+ years in AI/ML engineering, proficiency in Python, and experience with cloud platforms.
- Other info: Opportunity to join during seed growth stage and influence the company's direction.
The predicted salary is between 40000 - 64000 £ per year.
Location: Manchester - Hybrid / Home working
Job Type: Full-time
Salary: £50,000 - £80,000 base
We are looking for a mid-level AI / Machine Learning Engineer to join a vibrant early tech start-up in Manchester. Do you love turning machine learning models into intelligent, real-world applications? This is your chance to join a cutting-edge start-up team shaping the brainpower behind Behavioural biometrics and AI. Our client leverages behavioural biometric interactions and powerful AI to create unique user profiles for seamless security.
We are looking for candidates with expertise in sensor-based data, large behaviour data, or behavioural biometrics. Ideal applicants will have experience analysing and interpreting complex behavioural data to drive insights and innovation. You will be a skilled professional with a strong history of turning prototypes into robust, production-ready solutions that drive meaningful impact. This is your chance to join the early stages of growth and play a key role in shaping the future before they scale globally. Don't miss the opportunity to be part of the original team driving this innovation forward!
Due to recent investments and ambitious growth plans, they are looking for an AI Engineer to join the team.
The Role:
- As an AI / ML Engineer, you’ll transform complex behavioural data into responsive, intelligent, and scalable systems that think and adapt in real time.
- Architect and deploy machine learning models from idea to production.
- Build robust APIs and microservices to serve AI models at scale.
- Integrate behavioural intelligence models across cloud platforms (AWS, GCP, Azure).
- Set up end-to-end MLOps pipelines: monitoring, retraining, and automation.
- Collaborate with cross-functional teams to align tech with user-centric product design.
What We’re Looking For:
- ~2+ years in AI/ML engineering or backend software roles with ML components.
- ~Proficiency in Python and frameworks like PyTorch/TensorFlow, Scikit-learn.
- ~Experience deploying models with Docker, Kubernetes, or serverless architectures.
- ~Solid grasp of MLOps workflows, versioning, and cloud automation.
- ~Strong foundations in algorithms, data structures, and system design.
- ~Bonus: Familiarity with behavioural biometrics, sensor-based or time series data.
- ~An entrepreneurial mindset—curious, autonomous, and passionate about human-centred AI.
AWS Engineer employer: 55 Exec Search
Contact Detail:
55 Exec Search Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AWS Engineer
✨Tip Number 1
Familiarise yourself with the latest trends in AI and machine learning, especially in behavioural biometrics. This knowledge will not only help you during interviews but also show your genuine interest in the field.
✨Tip Number 2
Network with professionals in the AI/ML community, particularly those working in start-ups. Attend meetups or webinars to connect with potential colleagues and learn about their experiences, which can give you insights into the company culture.
✨Tip Number 3
Prepare to discuss your past projects in detail, especially those involving machine learning models and APIs. Be ready to explain your thought process, challenges faced, and how you overcame them, as this demonstrates your problem-solving skills.
✨Tip Number 4
Showcase your entrepreneurial mindset by discussing any personal projects or initiatives you've undertaken. This could be anything from contributing to open-source projects to developing your own AI applications, highlighting your passion and autonomy.
We think you need these skills to ace AWS Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in AI/ML engineering, particularly focusing on your work with behavioural data and machine learning models. Use keywords from the job description to ensure your application stands out.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for AI and machine learning. Discuss specific projects where you've transformed prototypes into production-ready solutions, and how your skills align with the company's mission.
Showcase Technical Skills: In your application, emphasise your proficiency in Python and any frameworks like PyTorch or TensorFlow. Mention your experience with Docker, Kubernetes, and MLOps workflows to demonstrate your technical capabilities.
Highlight Collaborative Experience: Since the role involves working with cross-functional teams, include examples of past collaborations. Describe how you’ve aligned technology with user-centric design, showcasing your ability to work well in a team environment.
How to prepare for a job interview at 55 Exec Search
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python and frameworks like PyTorch or TensorFlow. Bring examples of projects where you've deployed machine learning models, especially using Docker or Kubernetes.
✨Understand the Company’s Vision
Research the start-up's focus on behavioural biometrics and AI. Be ready to explain how your skills can contribute to their mission of creating unique user profiles for security.
✨Demonstrate Problem-Solving Abilities
Prepare to discuss specific challenges you've faced in previous roles and how you overcame them. Highlight your ability to turn prototypes into production-ready solutions.
✨Ask Insightful Questions
Engage with the interviewers by asking questions about their current projects, team dynamics, and future growth plans. This shows your interest in the role and helps you assess if it's the right fit for you.
