Software (SaMD) & Artificial intelligence (AI) Product Assessor - Active Devices

SGS
Ellesmere Port
11 months ago
Applications closed

Related Jobs

View all jobs

Software Engineer - AI MLOps Oxford, England, United Kingdom

Software Engineer III - MLOps

GenAI Software Engineer/Data Scientist

Data Engineer

Data Engineer

Data Engineer

Job Description

Are you ready to take your career to the next level? If you have a strong background as a Product Assessor within an EU notified body conducting MDS technical documentation review, then we have an exhilarating opportunity for you!

Join our dynamic team at SGS and become a Software & AI Product Assessor. This role will allow you to make a significant impact in the field of certification.

Your responsibilities will include:

Conducting thorough technical documentation reviews of SaMD and devices using AI/Machine Learning (ML) and ensuring compliance with EU regulations and standards. Collaborating with cross-functional teams to increase efficiency and quality Develop training and present the training to others in SGS or externally as deemed necessary. Project manage reviews as appropriate, to maximize efficiencies, enhance client satisfaction and ensure compliance with standard Support/assist on queries raised from the review Work at all times to adhere to KPI’s set as an individual and within a team Ensure personal competency is maintained to be able to review technical files and technical documentation Ensure that the highest level of service is provided throughout the SGS network offering medical devices certification and stakeholders through efficient service delivery.

Qualifications

Bachelor’s degree in a related discipline (, engineering, or other relevant sciences) Four years of professional experience in the field of healthcare products or related activities, such as design, manufacturing, auditing, or research, of which two years shall be in developing SaMD demonstrating knowledge of one or more programming languages (preferable in MDSW or other critical software requiring compliance with regulations) and AI/ML models in specific industry/academic research. Thorough knowledge and understanding of the Software Life Cycle processes: requirement analysis, design and coding configuration management including version and change control testing including design and coding, configuration management including version and change control, testing including unit, integration, regression and system testing, validation and release, and problem resolution techniques including debugging techniques, root cause analysis and code reviews. Knowledge and understanding of the related standards: IEC 62304, IEC 82304, IEC 81001-5-1, IEC 62366, etc… Thorough knowledge and understanding of AI regulations, released standards/guidance and AI/ML tools/libraries; Good written English skills (as reports will be reviewed/queried in English); Knowledge of the following Technical File codes desirable (COMMISSION IMPLEMENTING REGULATION (EU) 2017/2185 of 23 November 2017) MDS1009 and MDS0315.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.