Data Scientist/AI Engineer

Square One Resources
Birmingham
1 month ago
Applications closed

Related Jobs

View all jobs

Data Scientist / AI Engineer

Data Scientist (Government)

Data Scientist

Data Scientist

Data Scientist

Senior Data Scientist

Job Title: AI Engineer/Data Scientist


Location: Sheffield or Birmingham - 3 days per week in the office - only candidates within commutable distance will be considered by the client


Salary/Rate: 500 per day inside IR35


Start Date: 29/01/2026


Job Type: Contract - 6 months


Company Introduction

We have an exciting opportunity now available with one of our sector‑leading financial services clients! They are currently looking for a skilled AI Engineer/Data Scientist to join their team for an initial 6‑month contract.


Job Responsibilities/Objectives

The Onboarding and Know Your Customer Value Stream incorporates onboarding products, platforms, and a delivery capability particularly suited to client‑aligned agile delivery at pace. They are investing heavily across these domains with a strategic focus on increasing adoption of AI capabilities through our flagship AI journeys, day‑to‑day engineering and overall ways of working. To accelerate achieving our vision , we are seeking an experienced AI Engineer to join the Client Services and OBKYC Technology group.



  1. Building production‑ready models to drive content extraction and classification from images and text‑based sources.
  2. Working closely with business teams to understand requirements and iteratively design and develop solutions.
  3. Collaborative with product managers, technical teams.
  4. Create, test and iterate new and existing products and features.
  5. Designing and building Python/ML/OCR‑based components.
  6. Not only supporting the development of the product, but also the full lifecycle including the deployment, testing and production support of the application.

Required Skills/Experience

The ideal candidate will have the following:



  1. Strong experience in Document AI/Intelligent document processing using traditional models and Generative AI - particularly in using open source models for achieving business outcomes.
  2. Experience delivering to production in Python, with a focus on machine learning, deep learning, natural language processing, generative AI, image processing and OCR all additional positives.
  3. Experience with some of the following frameworks - TensorFlow, Pytorch, Hugging face, Spacy, OpenCV, Regex or equivalents.
  4. Experience delivering safe code to production, focusing on cybersecurity and resilience of the application and APIs.

Desirable Skills/Experience

Although not essential, the following skills are desired by the client:



  1. Experience using PostgreSQL for data storage and management.
  2. Proficiency with Azure core services like Azure Virtual Machines and experience with one or all of Azure CLI, Azure Kubernetes Service (AKS) and Azure DevOps
  3. Experience delivering in teams releasing at a high cadence to production.


#J-18808-Ljbffr

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 Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.