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For Companies At a Glance
- Tasks: Develop AI models to predict user health patterns and streamline deployment.
- Company: Join a pioneering health tech company focused on impactful AI solutions.
- Benefits: Enjoy flexible part-time hours, competitive pay, and remote work options.
- Why this job: Make a real difference in healthcare while working with industry experts.
- Qualifications: Experience in MLOps, data engineering, and cloud environments is essential.
- Other info: This is a 6-month contract role, outside IR35, based in the UK.
The predicted salary is between 81000 - 108000 £ per year.
Looking to make an impact with AI-driven health tech? We are hiring a Machine Learning Engineer for a part-time (3 days/week), outside IR35 contract role at £450 per day. If you are passionate about using AI to drive meaningful change, this could be for you.
The Role:
- Building AI that understands and predicts user health patterns.
- Developing machine learning models for personalised interventions.
- Implementing NLP and deep learning techniques where applicable.
- Designing and implementing end-to-end MLOps pipelines to streamline model deployment and monitoring.
- Automating model training, validation, and deployment workflows.
- Ensuring models are scalable, reproducible, and maintainable in production environments.
- Managing model versioning, drift detection, and continuous integration.
- Optimising data pipelines to support real-time and batch inference.
- Cleaning, processing, and optimising data for AI training.
- Designing scalable data pipelines to ensure high-quality model performance.
- Ensuring data is ethical, relevant, and aligned with health tech applications.
- Working closely with leadership, including ex-Microsoft talent, to align AI with company goals.
- Engaging with domain experts to refine AI-driven solutions.
- Contributing to long-term AI strategy and innovation.
User-Centric AI:
- Ensuring solutions are accessible, ethical, and impactful.
- Tailoring AI applications to diverse user needs.
- Prioritising explainability and transparency in AI models.
What You’ll Need:
- MLOps & Data Engineering experience – building scalable pipelines and automating workflows.
- Proficiency with tools like Kubeflow, MLflow, Airflow, Docker, Kubernetes, or similar.
- Experience in cloud environments (AWS, GCP, or Azure) for model deployment.
- Passion for AI in health tech and its real-world applications.
- Ability to work collaboratively with technical and non-technical stakeholders.
Why Join?
- Work on AI tech that makes a difference in healthcare.
- Flexible, part-time role – perfect for balancing other commitments.
- Competitive day rate with an outside IR35 contract structure.
- Be part of a growing team led by experts in AI & health tech.
Machine Learning Engineer employer: Golden Bees
Contact Detail:
Golden Bees Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Network with professionals in the AI and health tech sectors. Attend relevant meetups, webinars, or conferences to connect with industry experts and learn about potential job openings. Engaging with the community can lead to valuable referrals.
✨Tip Number 2
Showcase your projects related to MLOps and data engineering on platforms like GitHub or LinkedIn. Highlight any experience you have with tools such as Kubeflow or Docker, as this will demonstrate your hands-on skills and passion for the role.
✨Tip Number 3
Prepare to discuss real-world applications of AI in health tech during interviews. Familiarise yourself with current trends and challenges in the industry, as this will show your enthusiasm and understanding of how your skills can contribute to meaningful change.
✨Tip Number 4
Be ready to explain your approach to ensuring ethical AI practices. Given the focus on user-centric solutions, demonstrating your commitment to transparency and explainability in AI models will set you apart from other candidates.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in MLOps and data engineering. Include specific projects where you've built scalable pipelines or automated workflows, especially in health tech.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for AI in health tech. Mention how your skills align with the job requirements, particularly your experience with tools like Kubeflow, MLflow, and cloud environments.
Showcase Your Projects: If you have any personal or professional projects related to machine learning or AI, include them in your application. Highlight your role in these projects and the impact they had, especially in user-centric applications.
Highlight Collaboration Skills: Since the role involves working with both technical and non-technical stakeholders, emphasise your ability to communicate complex ideas clearly. Provide examples of past collaborations that demonstrate this skill.
How to prepare for a job interview at Golden Bees
✨Showcase Your MLOps Expertise
Be prepared to discuss your experience with MLOps and data engineering. Highlight specific projects where you've built scalable pipelines or automated workflows, as this is crucial for the role.
✨Demonstrate Your Passion for Health Tech
Express your enthusiasm for using AI in health tech during the interview. Share examples of how you've applied machine learning to real-world problems, particularly in healthcare settings.
✨Familiarise Yourself with Relevant Tools
Make sure you know the tools mentioned in the job description, such as Kubeflow, MLflow, and Docker. Be ready to discuss how you've used these tools in past projects to enhance your credibility.
✨Prepare for Collaborative Scenarios
Since the role involves working with both technical and non-technical stakeholders, think of examples that demonstrate your ability to communicate complex ideas clearly and work collaboratively across teams.
