Manufacturing Support Engineer

Newcastle upon Tyne
6 days ago
Create job alert

Zenith People are working with our client who are a Manufacturer based in the North East and are looking to recruit an experienced Manufacturing Support Engineer to the business.

The main purpose of this role is to undertake Industrial Innovation projects, aimed to increase the efficiency and productivity Operations through digital transformation and automation. You will support all Operation Teams with their processes, working alongside the Manufacturing Engineer helping to provide a broad range of skills to assist in implementing improvements. The role will support directly to the Operations Support Manager whilst working closely with the industrial Innovation Support Manager.

Duties & Responsibilities

  • Cooperate with all Operational teams to help to identify new Industrial Innovation projects

  • Modify current improvements to bring their effectiveness up to speed

  • Management improvement projects , i.e.

    • Automating manual company processes with the aid of software or robotics

    • Develop ‘live progress’ tracking on production lines and associated KPI’s

    • Introduce automated visual inspection with the aid of cameras

  • Take responsibility for the software and hardware development, establishing robust troubleshooting and maintenance plans

    Skills & Experience Required

    The below are all desirable however would accept some gaps in knowledge if candidate is willing to learn and progress to develop their skills.

  • Understanding of basic wiring diagram, both power/control parts, low voltage power distribution, I/O and PLC connection

  • The ability to understand business concepts

  • Understanding of cloud platforms and API

  • Understanding the Full Stacks (database, python / C++, JavaScript, Cloud System, API)

  • Knowledge of server and network architecture

  • Basic knowledge of Microcontroller Board and/or Industrial PLC, Modbus / BACnet protocols

  • Knowledge of main control algorithm theory, basic knowledge in the computer vision

  • Basic knowledge of Industrial Robots

  • Preferably having 1 year experience software development related to automation project

    Qualifications

  • Degree in relevant field (Computer Science, Data Science, Automation Engineering)

  • Desirable to have additional training in any of the following:

    • Industry 4.0 technologies

    • IoT

    • AI

    • Digital Manufacturing

      If you would like to be considered for this vacancy please click apply now

Related Jobs

View all jobs

Graduate Manufacturing Support Engineer

Control Systems Development Engineer

Senior Principle Electrical Engineer

Senior Principle Electrical Engineer

HVDC Control & Protection Engineering Manager

Senior Electrical Design Engineer - Hybrid Working

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Tips for Staying Inspired: How Machine Learning Pros Fuel Creativity and Innovation

Machine learning (ML) continues to reshape industries—from personalised e-commerce recommendations and autonomous vehicles to advanced healthcare diagnostics and predictive maintenance in manufacturing. Yet behind every revolutionary model lies a challenging and sometimes repetitive process: data cleaning, hyperparameter tuning, infrastructure management, stakeholder communications, and constant performance monitoring. It’s no wonder many ML professionals can experience creative fatigue or get stuck in the daily grind. So, how do machine learning experts keep their spark alive and continually generate fresh ideas? Below, you’ll find ten actionable strategies that successful ML engineers, data scientists, and research scientists use to stay innovative and push boundaries. Whether you’re an experienced practitioner or just breaking into the field, these tips can help you fuel creativity and discover new angles for solving complex problems.

Top 10 Machine Learning Career Myths Debunked: Key Facts for Aspiring Professionals

Machine learning (ML) has become one of the hottest fields in technology—touching everything from recommendation engines and self-driving cars to language translation and healthcare diagnostics. The immense potential of ML, combined with attractive compensation packages and high-profile success stories, has spurred countless professionals and students to explore this career path. Yet, despite the boom in demand and innovation, machine learning is not exempt from myths and misconceptions. At MachineLearningJobs.co.uk, we’ve had front-row seats to the real-life career journeys and hiring needs in this field. We see, time and again, that outdated assumptions—like needing a PhD from a top university or that ML is purely about deep neural networks—can mislead new entrants and even deter seasoned professionals from making a successful transition. If you’re curious about a career in machine learning or looking to take your existing ML expertise to the next level, this article is for you. Below, we debunk 10 of the most persistent myths about machine learning careers and offer a clear-eyed view of the essential skills, opportunities, and realistic paths forward. By the end, you’ll be better equipped to make informed decisions about your future in this dynamic and rewarding domain.

Global vs. Local: Comparing the UK Machine Learning Job Market to International Landscapes

How to evaluate opportunities, salaries, and work culture in machine learning across the UK, the US, Europe, and Asia Machine learning (ML) has rapidly transcended the research labs of academia to become a foundational pillar of modern technology. From recommendation engines and autonomous vehicles to fraud detection and personalised healthcare, machine learning techniques are increasingly ubiquitous, transforming how organisations operate. This surge in applications has fuelled an extraordinary global demand for ML professionals—data scientists, ML engineers, research scientists, and more. In this article, we’ll examine how the UK machine learning job market compares to prominent international hubs, including the United States, Europe, and Asia. We’ll explore hiring trends, salary ranges, workplace cultures, and the nuances of remote and overseas roles. Whether you’re a fresh graduate aiming to break into the field, a software engineer with an ML specialisation, or a seasoned professional seeking your next challenge, understanding the global ML landscape is essential for making an informed career move. By the end of this overview, you’ll be equipped with insights into which regions offer the best blend of salaries, work-life balance, and cutting-edge projects—plus practical tips on how to succeed in a domain that’s constantly evolving. Let’s dive in.