AI Engineer

Digbeth
1 year ago
Applications closed

Related Jobs

View all jobs

AI Engineer – (Quantexa/Fraud & Financial Crime/ETL/MLOps/CI/CD/Azure/Insurance)

AI Engineer – (Quantexa/Fraud & Financial Crime/ETL/MLOps/CI/CD/Azure/Insurance)

AI Engineer / Data Scientist - Production ML & OCR

Senior AI Engineer | GenAI | Information Retrieval | ML | NLP | Hybrid London | Up to £130k

Senior AI Engineer | GenAI | Information Retrieval | ML | NLP | Hybrid London | Up to £130k

Machine Learning Engineer

Job Title: AI Engineer
Location: Birmingham (hybrid)

Salary: £40-50k

Roles and Responsibilities

As an AI Engineer, you will play a pivotal role in developing and deploying cutting-edge AI solutions to solve real-world challenges. Your key responsibilities will include:

Designing, developing, and deploying AI/ML models and algorithms to address business needs.
Working with cross-functional teams to integrate AI solutions into existing systems or develop new AI-driven products.
Monitoring the performance of AI models post-deployment and making necessary adjustments for continuous improvement.
Leveraging LLMs to assist in automating and optimising code generation, reducing development time and improving efficiency.
Developing tools and workflows powered by LLMs to support software engineers in debugging, refactoring, and enhancing code quality.
Implementing LLM-based solutions to streamline documentation creation, code review processes, and knowledge sharing across teams.
Documenting technical processes and presenting findings to stakeholders in a clear and concise manner.Key Skills and Qualifications

We are looking for a talented AI Engineer with the following skills and experience:

Technical Skills

Proficiency in programming languages such as Python & JavaScript
Strong understanding of machine learning frameworks (e.g., TensorFlow, PyTorch, Keras).
Experience with data preprocessing tools and libraries (e.g., pandas, NumPy, scikit-learn).
Familiarity with technologies such as React & Next.js
Experience with LLMs such as GPT4.
Familiarity with cloud platforms such as AWS, Azure, or Google Cloud for AI/ML deployments.
Knowledge of big data technologies like Spark, Hadoop, or Kafka is a plus.
Proficient in version control tools like Git and CI/CD pipelines for machine learning workflows.Other Requirements

A degree in Computer Science, Data Science, Mathematics, or a related field. A Master's/PhD is a plus.
2+ years of experience in AI/ML development or a related role.
Strong problem-solving and analytical skills.
Excellent communication skills and ability to work collaboratively in a team environment.Why Join Us?

Work on cutting-edge AI projects with a talented and passionate team.
Huge emphasis placed on AI tools and technologies.
Opportunities for career growth and professional development.
Access to the latest AI tools and resources.
25 days annual leave + bank holidays, pension contributions, private healthcareHow to Apply
Interested? Submit your CV detailing your relevant experience to apply today!

BeTechnology Group Limited is acting as an Employment Agency in relation to this vacancy

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 to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.