Microsoft Azure ML Engineer

Version 1
Newcastle upon Tyne
1 year ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Senior Machine Learning Operations Engineer

Senior Data Scientist

Artificial Intelligence Architect

Artificial Intelligence Architect

Machine Learning Engineer (Manager)

Job Description

  • Apply machine learning, deep learning, natural language processing, computer vision, and other AI techniques to various domains and use cases, to analyse large datasets and extract meaningful patterns and insights. Develop predictive models and algorithms to support business objectives.
  • Explore and pre-process raw data to uncover trends, anomalies, and patterns.
  • Create clear and compelling visualizations to communicate findings to both technical and non-technical stakeholders.
  • Evaluate and optimize the performance, accuracy, and scalability of AI models and systems.
  • Research and implement state-of-the-art AI methods and best practices.
  • Collaborate with cross-functional teams of engineers, product managers, designers, and stakeholders, to understand business requirements and formulate data-driven solutions.
  • Collaborate with domain experts to integrate domain knowledge into analytical models.
  • Prototype, test, and productionise machine learning models. Monitor and optimize model performance over time.
  • Model data for storage and querying in database and large-scale analytics platforms.
  • Work with a wide variety of data types, from structured to unstructured, multimedia (video and still images), time-series and IoT data.
  • Clearly visualise and communicate processes and results to clients, colleagues and other stakeholders.
  • Support more junior colleagues and help develop the capabilities of the team.
  • Work with colleagues across the firm to apply data science principles to specialist areas of the clients business.


Qualifications

  • Hands-on experience with using, configuring and migrating ML models to Azure ML specifically a Kubeflow/Tensorflow stack (on prem), to a full managed Azure ML solution.
  • A passion for developing and communicating deep and valuable insights.
  • A highly rigorous mindset and approach to your work.
  • In-depth knowledge of statistics and research methodology/experimental design.
  • Extensive, demonstrable experience building and testing/validating machine learning models and using data mining techniques.
  • Extensive experience of data modelling and database design.
  • Demonstrable programming experience in Python and associated data science/ML libraries in Azure ML Tech Stack.
  • The ability to form clear narratives that communicate complex ideas to diverse audiences.
  • Experience of team leadership / supervising others.
  • Experience of proposal writing, project costing, project management, etc.
  • Ability to quickly make sense of new topics in unfamiliar fields.
  • Specialist experience in a particular domain/application of data science, e.g. natural language processing, remote sensing, IoT.
  • Experience of team leadership / supervising others.
  • Ability to quickly make sense of new topics in unfamiliar fields.
  • A Bachelor or Master degree in a computational field (Computer Science, Applied Mathematics, Engineering, or in a related quantitative discipline).
  • Azure Certified Machine Learning Certification would be desirable.



Additional Information

At Version 1, we believe in providing our employees with a comprehensive benefits package that prioritises their well-being, professional growth, and financial stability.

One of our standout advantages is the ability to work with a hybrid schedule along with business travel, allowing our employees to strike a balance between work and life. We also offer a range of tech-related benefits, including an innovative Tech Scheme to help keep our team members up-to-date with the latest technology.

We prioritise the health and safety of our employees, providing private medical and life insurance coverage, as well as free eye tests and contributions towards glasses. Our team members can also stay ahead of the curve with incentivized certifications and accreditations, including AWS, Microsoft, Oracle, and Red Hat.

Our employee-designed Profit Share scheme divides a portion of our company's profits each quarter amongst employees. We are dedicated to helping our employees reach their full potential, offering Pathways Career Development Quarterly, a programme designed to support professional growth.

Laura Cowan

#LI-LC1

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 AI Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are a software engineer, data scientist or analyst looking to move into AI or you are a UK undergraduate or postgraduate in computer science, maths, engineering or a related subject applying for AI roles, the maths can feel like the biggest barrier. Job descriptions say “strong maths” or “solid fundamentals” but rarely spell out what that means day to day. The good news is you do not need a full maths degree worth of theory to start applying. For most UK roles like Machine Learning Engineer, AI Engineer, Data Scientist, Applied Scientist, NLP Engineer or Computer Vision Engineer, the maths you actually use again & again is concentrated in a handful of topics: Linear algebra essentials Probability & statistics for uncertainty & evaluation Calculus essentials for gradients & backprop Optimisation basics for training & tuning A small amount of discrete maths for practical reasoning This guide turns vague requirements into a clear checklist, a 6-week learning plan & portfolio projects that prove you can translate maths into working code.

Neurodiversity in AI Careers: Turning Different Thinking into a Superpower

The AI industry moves quickly, breaks rules & rewards people who see the world differently. That makes it a natural home for many neurodivergent people – including those with ADHD, autism & dyslexia. If you’re neurodivergent & considering a career in artificial intelligence, you might have been told your brain is “too much”, “too scattered” or “too different” for a technical field. In reality, many of the strengths that come with ADHD, autism & dyslexia map beautifully onto AI work – from spotting patterns in data to creative problem-solving & deep focus. This guide is written for AI job seekers in the UK. We’ll explore: What neurodiversity means in an AI context How ADHD, autism & dyslexia strengths match specific AI roles Practical workplace adjustments you can ask for under UK law How to talk about your neurodivergence during applications & interviews By the end, you’ll have a clearer picture of where you might thrive in AI – & how to set yourself up for success.

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

As we head into 2026, the AI hiring market in the UK is going through one of its biggest shake-ups yet. Economic conditions are still tight, some employers are cutting headcount, & AI itself is automating whole chunks of work. At the same time, demand for strong AI talent is still rising, salaries for in-demand skills remain high, & new roles are emerging around AI safety, governance & automation. Whether you are an AI job seeker planning your next move or a recruiter trying to build teams in a volatile market, understanding the key AI hiring trends for 2026 will help you stay ahead. This guide breaks down the most important trends to watch, what they mean in practice, & how to adapt – with practical actions for both candidates & hiring teams.