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- Tasks: Join a team to design and deploy cutting-edge AI solutions in medical devices.
- Company: Be part of Stryker’s innovative AI unit, transforming healthcare technology.
- Benefits: Enjoy hybrid work flexibility, competitive salary, and opportunities for professional growth.
- Why this job: Make a real impact in healthcare with advanced AI and GenAI technologies.
- Qualifications: Bachelor's degree in relevant field and 4+ years of experience in AI/ML development.
- Other info: Collaborate with industry leaders and work on diverse therapeutic areas.
The predicted salary is between 48000 - 72000 £ per year.
Work Flexibility: Hybrid or Onsite
As a Senior AI Deployment Engineer, you will work along with a team of AI scientists, AI and xR application engineers, software engineers, and clinical experts to design, develop, and deploy computer vision, augmented reality, mixed reality, multi-modal AI models, and GenAI features into existing and new medical device products. This is a unique, high visibility opportunity for a talented individual who wants to dive deep into cutting-edge AI and GenAI optimization for cloud and edge deployments.
- Develop and deploy Artificial Intelligence (AI) powered software on cloud and edge devices (iPhone, iPad, Vision Pro, Android devices, NVidia devices).
- Optimize AI/ML models and pipelines for real-time inference on edge devices.
- Build containerized AI services (e.g. Docker) and orchestrate deployments.
- Deploy and maintain real-time microservices for AI applications including GenAI apps.
- Work with MLOps and AI security platform team to support continuous integration, testing, and monitoring of AI models.
- Design deployment evaluation frameworks, develop unit tests for software components in compliance with regulatory requirements.
- Generate and review the necessary documents with project teams.
- Perform Software verification and/or validation testing.
- Perform code reviews as an independent reviewer following best coding standards and practices.
Qualifications:
- Bachelor's degree in software engineering, Computer Science, or related discipline with 2+ years of relevant work experience OR Master’s in relevant disciplines OR PhD degree in relevant disciplines.
- At least 4+ years of Python and C++ development experience.
- 3+ years of experience developing and deploying AI/ML models into production environments.
- Proficiency in containerization tools (Docker, Docker Compose).
- Experience with CI/CD Git automation pipelines.
- Strong Cloud deployment experience (Azure, GCP, or AWS).
- Proven ability to optimize AI models for real-time inference on edge devices to meet latency and performance requirements.
- Knowledge of model optimization techniques (ONNX Runtime, quantization, pruning, TensorRT, OpenVINO, CoreML, etc.).
- Experience with voice, LLMs, and Generative AI (LLMs, vision, multimodal, multiagent) microservices experience.
- Experience with multi-agentic AI frameworks for orchestrating complex workflows.
- Experience with Kubernetes for orchestrating AI microservices.
- Familiarity with monitoring and logging (Prometheus, Grafana, Azure Monitor, etc.).
- Experience with medical devices and product development standards in a regulated environment (ISO 13485, IEC 62304, ISO 14971).
As a valued member of Stryker’s AI innovation unit, you will work alongside trailblazers, industry visionaries, innovators, and inventors who are committed to bringing computer vision, machine learning, and generative AI and digital innovation to the operating room and other healthcare settings. You’ll contribute to fast-paced cycles of innovation and develop core technologies that power a wide array of Stryker’s solutions, including surgical robotics and navigation, image-guided surgery, treatment selection, outcome assessment, and clinical decision intelligence. You will apply your core skills across a range of deployment platforms spanning from mobile applications, cloud services, and SDKs to embedded systems, edge devices, and mixed reality (XR) platforms. You will have an opportunity to work across a wide variety of therapeutic areas ranging from orthopedics and neurosurgery to emergency care and operating room safety and efficiency – plus many more.
Travel Percentage: 10%
Senior AI Deployment Engineer in London employer: Stryker European Operations Limited - Dutch Branch Office
Contact Detail:
Stryker European Operations Limited - Dutch Branch Office Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior AI Deployment Engineer in London
✨Network Like a Pro
Get out there and connect with folks in the industry! Attend meetups, webinars, or conferences related to AI and medical devices. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Show Off Your Skills
When you get the chance to chat with potential employers, don’t hold back! Share your experiences with Python, C++, and any cool AI projects you've worked on. Make sure they see how you can bring value to their team.
✨Tailor Your Approach
Before interviews, do your homework on the company and its products. Tailor your answers to show how your skills in AI deployment and model optimization fit perfectly with what they’re doing. It’ll make you stand out!
✨Apply Through Our Website
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team at Stryker.
We think you need these skills to ace Senior AI Deployment Engineer in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the Senior AI Deployment Engineer role. Highlight your Python and C++ experience, as well as any work with AI/ML models and cloud deployments. We want to see how you fit into our innovative team!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to tell us why you're passionate about AI and how your background aligns with our mission at StudySmarter. Be sure to mention any relevant projects or achievements that showcase your expertise.
Showcase Your Projects: If you've worked on any cool AI projects, don't hold back! Include links to your GitHub or any other platforms where we can see your work. This gives us a better idea of your hands-on experience and creativity in deploying AI solutions.
Apply Through Our Website: We encourage you to apply directly through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to submit all your materials in one go. Plus, it shows us you’re serious about joining our team!
How to prepare for a job interview at Stryker European Operations Limited - Dutch Branch Office
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technologies mentioned in the job description, especially Python, C++, and containerization tools like Docker. Brush up on your knowledge of AI/ML model optimisation techniques and cloud deployment platforms such as Azure, GCP, or AWS. Being able to discuss these topics confidently will show that you're ready to hit the ground running.
✨Showcase Your Experience
Prepare specific examples from your past work where you've successfully developed and deployed AI models or worked with microservices. Highlight any experience you have with medical devices and regulatory standards, as this will be crucial for the role. Use the STAR method (Situation, Task, Action, Result) to structure your responses.
✨Ask Insightful Questions
Interviews are a two-way street! Prepare thoughtful questions about the team dynamics, current projects, and the company’s vision for AI in healthcare. This not only shows your interest but also helps you gauge if the company culture aligns with your values and career goals.
✨Demonstrate Your Problem-Solving Skills
Be ready to tackle hypothetical scenarios or technical challenges during the interview. Think aloud as you work through problems, showcasing your analytical thinking and approach to troubleshooting. This will give the interviewers insight into how you handle real-world issues in AI deployment.
