Artificial Intelligence Engineer

MBN Solutions
3 months ago
Applications closed

Related Jobs

View all jobs

Director of Artificial Intelligence - Manufacturing & Industrial

AI Engineer / Data Scientist

Senior Recruiter

Principal Data Scientist - Remote

Principal Data Scientist (Remote)

Senior Data Scientist

AI Engineer – Startup (AI Consultancy) - London (Hybrid) Upto £95k +sharesWe’re working with a fast growth AI Consultancy with offices in London, Manchester and Edinburgh. The business is under a year old and currently sitting at 15 people, generating revenue with a range of AI projects including fact extraction, conversational AI, Co-pilots, knowledge graphs, spanning across a variety of clients such as Investments, Insurance, F&B and CPG.The founders have proven experience in building a consultancy from scratch and exiting, having previously built a Data Science consultancy and successfully sold to a ‘big 4’, they are on course to replicate their success here.This has fuelled rapid growth in the team and we are looking for AI Engineers of different experience levels to join us in building the business by developing end2end LLM products for our clients.What we’re looking for:We’re looking for people that have experience building production ready AI solutions, to design, build and deploy AI solutions in collaboration with our clients. You’ll be applying some of the SOTA research in AI to develop applications. As such we would expect you to have:Effective communication skillsBackground in foundational Computer Vision/NLPStrong Software Engineering skills (3 years+)Developed LLM architecture and deployed LLM applicationsUptodate with current trends in AISome experience with applying latest techniques like RAG architecture, GenAI, Parallel training etcThe role is hybrid, with adhoc requirements to be on client premises (London) this could be between 1-5 days a week, so we would need someone that is flexible.This is a fantastic opportunity to work on a broad range of problems, applying SOTA AI solutions, in a high growth business, set for success and you will be rewarded with:Base salary of upto £95kMeaningful EMI shares (our proven history speaks for itself)25 Days holidayStatutory pension contributionPrivate medicalPlease note: you must be eligible to work in the UK to be considered for this position.Interested?If you think you fit the bill, get in touch by clicking the ‘apply now’ button or get in touch with me by the following:Email me at me on

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Jobs

Portfolio Projects That Get You Hired for Data Science Jobs (With Real GitHub Examples)

Data science is at the forefront of innovation, enabling organisations to turn vast amounts of data into actionable insights. Whether it’s building predictive models, performing exploratory analyses, or designing end-to-end machine learning solutions, data scientists are in high demand across every sector. But how can you stand out in a crowded job market? Alongside a solid CV, a well-curated data science portfolio often makes the difference between getting an interview and getting overlooked. In this comprehensive guide, we’ll explore: Why a data science portfolio is essential for job seekers. Selecting projects that align with your target data science roles. Real GitHub examples showcasing best practices. Actionable project ideas you can build right now. Best ways to present your projects and ensure recruiters can find them easily. By the end, you’ll be equipped to craft a compelling portfolio that proves your skills in a tangible way. And when you’re ready for your next career move, remember to upload your CV on DataScience-Jobs.co.uk so that your newly showcased work can be discovered by employers looking for exactly what you have to offer.

Jobs Careers

Data Science Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Data science has become one of the most sought‑after fields in technology, leveraging mathematics, statistics, machine learning, and programming to derive valuable insights from data. Organisations across every sector—finance, healthcare, retail, government—rely on data scientists to build predictive models, understand patterns, and shape strategy with data‑driven decisions. If you’re gearing up for a data science interview, expect a well‑rounded evaluation. Beyond statistics and algorithms, many roles also require data wrangling, visualisation, software engineering, and communication skills. Interviewers want to see if you can slice and dice messy datasets, design experiments, and scale ML models to production. In this guide, we’ll explore 30 real coding & system‑design questions commonly posed in data science interviews. You’ll find challenges ranging from algorithmic coding and statistical puzzle‑solving to the architectural side of building data science platforms in real‑world settings. By practising with these questions, you’ll gain the confidence and clarity needed to stand out among competitive candidates. And if you’re actively seeking data science opportunities in the UK, be sure to visit www.datascience-jobs.co.uk. It’s a comprehensive hub featuring junior, mid‑level, and senior data science vacancies—spanning start‑ups to FTSE 100 companies. Let’s dive into what you need to know.

Jobs

Negotiating Your Data Science Job Offer: Equity, Bonuses & Perks Explained

Data science has rapidly evolved from a niche specialty to a cornerstone of strategic decision-making in virtually every industry—from finance and healthcare to retail, entertainment, and AI research. As a mid‑senior data scientist, you’re not just running predictive models or generating dashboards; you’re shaping business strategy, product innovation, and customer experiences. This level of influence is why employers are increasingly offering compensation packages that go beyond a baseline salary. Yet, many professionals still tend to focus almost exclusively on base pay when negotiating a new role. This can be a costly oversight. Companies vying for data science talent—especially in the UK, where demand often outstrips supply—routinely offer equity, bonuses, flexible work options, and professional development funds in addition to salary. Recognising these opportunities and effectively negotiating them can have a substantial impact on your total earnings and long-term career satisfaction. This guide explores every facet of negotiating a data science job offer—from understanding equity structures and bonus schemes to weighing crucial perks like remote work and ongoing skill development. By the end, you’ll be well-equipped to secure a holistic package aligned with your market value, your life goals, and the tremendous impact you bring to any organisation.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.