Machine Learning Engineer

Techfueld
Manchester
1 year ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer - Production ML & Big Data Pipelines

Data Scientist

Data Scientist

Hybrid AI/ML Engineer — Healthcare Predictive Analytics

London Data Scientist & ML Engineer | AWS, Python

Head of Data Science & ML Engineering

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.

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.

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.