Artificial Intelligence Engineer

Sanderson
Newcastle upon Tyne
1 year ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Scientist

Data Science Trainee

Data Science Trainee

Junior / Graduate AI Developer – Contract (Python / Machine Learning)

A high number of candidates may make applications for this position, so make sure to send your CV and application through as soon as possible.Remote:

Remote / Cardiff HQLength:

6 Months Initial + ExtensionsIndustry:

FTSE 100 – InsuranceWe are looking for an enthusiastic Junior or Graduate AI Developer with a passion for AI and Machine Learning. This role is ideal for someone eager to develop their skills in Python, cloud platforms, and AI/ML technologies while working in a supportive environment on real-world AI solutions. You’ll collaborate with experienced engineers and data scientists to learn and grow in the exciting field of AI development.Key Responsibilities:Assist in developing and maintaining Python-based AI applications and solutions.Support the training and deployment of machine learning models, including Generative AI models (e.g., language models, recommendation systems).Collaborate with data scientists to implement AI models into production environments.Help develop and test machine learning pipelines using cloud services such as Google Cloud Platform (GCP) or Microsoft Azure.Contribute to the development of APIs and microservices for AI-based solutions.Assist in setting up and managing cloud infrastructure for data processing and model deployment.Participate in code reviews, documentation, and discussions around AI best practices.Continuously learn and explore new AI technologies, frameworks, and methodologies.Preferred Skills and Experience:We’re open to candidates with varying levels of experience. If you don’t meet all the requirements but are eager to learn, we still encourage you to apply!Basic experience with Python and a willingness to learn popular ML frameworks like TensorFlow, PyTorch, or Scikit-learn.Familiarity with machine learning concepts, including supervised and unsupervised learning, is a plus.Some exposure to cloud platforms (GCP, Azure, or AWS) is beneficial, but not mandatory.An interest in Generative AI technologies (e.g., GPT models, NLP, or image generation) is highly desirable.Understanding of RESTful APIs and web services is a plus.Familiarity with version control systems like Git.Soft Skills:Strong desire to learn and grow in the field of AI development.Good communication and teamwork skills, with the ability to collaborate effectively with cross-functional teams.A proactive attitude toward problem-solving and a willingness to take on new challenges.Attention to detail and a drive for delivering high-quality work.Why Join Us?Opportunity to work on exciting AI/ML projects in a large-scale enterprise environment.Mentorship and guidance from experienced AI engineers and data scientists.Exposure to cutting-edge Generative AI and cloud technologies.A supportive environment where you can develop both technical and professional skills.Long-term potential for growth and extensions on a high-profile project.If you’re passionate about AI development, eager to work with real-world data, and keen to gain hands-on experience with cutting-edge technologies, we’d love to hear from you!

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.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.