ML Data Engineer

Experis
Cheshire East
6 months ago
Applications closed

Related Jobs

View all jobs

Principal Data Engineer

Principal Data Engineer

Principal Data Engineer

Lead Data Scientist

Lead Data/Head of Data Engineer

Senior Data engineer - Databricks

Location: Knutsford Job Type: Contract Industry: Digital Workspace Job reference: BBBH421254_1756200099 Posted: about 10 hours ago

Role Title: ML Data Engineer
Start Date: ASAP
End Date: EOY
Location: Knutsford (Hybrid)
Rate: £410p/d via Umbrella
Role Description:

Overview
We are seeking a highly capable Data & ML Engineer with strong experience in AWS-based machine learning pipelines, MLOps, and cloud-native deployment. This role focuses on building scalable data workflows, deploying ML models, and managing the full AI lifecycle in production environments.
Key Skills & Technologies
Primary Skills:

AWS Data Engineering: ECS, SageMaker, cloud-native data pipelines ML Engineering & MLOps: MLflow, Airflow, Docker, Kubernetes CI/CD & DevOps: GitLab, Jenkins, automated deployment workflows AI Lifecycle Management: Model training, deployment, monitoring Front-End Development: HTML, Streamlit, Flask (for lightweight dashboards and interfaces) Cloud Model Deployment: Experience deploying and monitoring models in AWS Programming & Big Data: Python, PySpark, familiarity with big data ecosystems

Secondary Skills:

RESTful APIs: Integration of backend services and model endpoints

Responsibilities

Build and maintain robust data pipelines and ML workflows on AWS Develop and deploy machine learning models using SageMaker and MLOps tools Implement CI/CD pipelines for automated testing and deployment Create lightweight front-end interfaces for model interaction and visualization Monitor model performance and ensure reliability in production environments Collaborate with data scientists and engineers to streamline the AI lifecycle

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.

Careers

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.

Jobs

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.

Education

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.

🍪 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.