Machine Learning Engineer

Techfueld
Manchester
1 year ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Head of Data Science

Data Engineer - (Python, SQL, Machine Learning) - Robotics

Data Engineer - DV Cleared

Full Stack Data Engineer

Full Stack Data Engineer (SC cleared)

About the Client

Our client is a dynamic startup who are revolutionizing the future of AI and machine learning applications. They are dedicated to pushing the boundaries of technology to create innovative solutions that drive real-world impact. Initially focused on the automotive market their technology will be able to be utilized in cross-industry as they grow.



Overview of the Role

Our client is searching for a hands-on AI/ML expert who is passionate about building new technologies to support building a new first-of-its-kind product on the market.



Responsibilities

Develop concepts for prompt engineering and multi-agent collaboration systems

Implement and fine-tune large language models (LLMs) for various applications

Implement and fine-tune TAG systems

Conduct testing and evaluation of AI models, ensuring robustness and performance

Stay up-to-date with the latest advancements in AI and machine learning research



Mandatory skills

  • Proficiency in Python programming language
  • Experience with large language models (LLMs), prompt engineering, and RAG pipelines
  • Hands-on experience with LLM agent orchestration frameworks



Skills and Qualifications

  • Bachelor's degree in Computer Science, Engineering, or related field
  • Experience with model fine-tuning techniques
  • Experience with cloud platforms



Who is Techfueld?

Techfueld is a specialist search firm focused solely within E-mobility & Vehicle Technology recruitment. We offer retained, contingent and project/team builds for automotive suppliers and car manufacturers across Europe, North America, and APAC regions.

All conversations are held confidentially, and your information is only forwarded to any of our clients, should you wish to proceed.

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.