DevOps & IoT Platform Engineer

Watson-Marlow Fluid Technology Solutions; Redruth; Hybrid; GBP 65,000 - 90,000; Full time

Job summary

Watson-Marlow Fluid Technology Solutions is seeking a DevOps & IoT Platform Engineer to support the build, operation, automation and reliability of the digital infrastructure underpinning connected products and industrial IoT capabilities.

The role focuses on DevOps, cloud platform operations, IoT data flows, deployment automation, environment management, monitoring and secure integration. It also offers a path into MLOps and ML platform engineering as the capability matures.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Data Science, Software Engineering, or related discipline (or equivalent experience)
• Experience in DevOps, platform engineering, cloud operations, software deployment, or infrastructure automation
• Experience with CI/CD pipelines, source control, branching strategies, pull requests, and release workflows using GitHub, Azure DevOps, or similar tools
• Exposure to Azure cloud services and operational practices, including monitoring, access control, environment management, and secure configuration
• Experience working with IoT, telemetry, time-series, connected-product, or industrial data platforms
• Proficiency in scripting or automation using Python, PowerShell, Bash, or equivalent tooling
• Understanding of data pipelines, APIs, integration patterns, and operational troubleshooting
• Strong problem-solving skills with the ability to work across software, data, cloud, and connected-product teams
• Experience with Azure IoT services, Azure Data Explorer, Databricks, containerisation, APIs, or cloud-native application deployment
• Experience with infrastructure as code, automated testing, observability, logging, or platform monitoring
• Awareness of MLOps concepts such as MLflow, model registry, model promotion, model monitoring, and governed deployment pipelines
• Exposure to machine learning workflows or data science environments, with an interest in developing into ML platform engineering
• Experience working with industrial equipment, embedded systems, firmware teams, or device-to-cloud architectures

Responsibilities

• Build, maintain and improve automation for cloud and IoT platform services that support connected products.
• Develop and maintain CI/CD pipelines, deployment workflows, environment controls and repeatable release processes using tools such as GitHub, Azure DevOps and related platform tooling.
• Help maintain Azure-based development, test, QA and production environments used by digital, IoT, data and ML platforms.
• Contribute to secure platform configuration, access control, secrets management, monitoring and operational governance.
• Monitor platform health, investigate operational issues and contribute to improving reliability, supportability and repeatability across digital product environments.
• Support reliable device-to-cloud data flows from connected products, gateways, cloud services and downstream data platforms.
• Work with telemetry, time-series and industrial IoT data sources, helping to ensure data is available, structured and usable for engineering, product and analytics teams.
• Support operational data pipelines that provide reliable data for engineering analytics, product insights, connected services and machine learning use cases.
• Assist with troubleshooting data ingestion, connectivity, data quality and integration issues across the connected-products ecosystem.
• Document deployment steps, support processes and platform knowledge to improve maintainability and reduce reliance on manual intervention.
• Work with IT, cyber security, software, firmware and supplier teams to support service availability, incident resolution and controlled platform change.
• Contribute to data validation, monitoring, transformation and handover between operational platforms and analytical environments.
• Proactively identify opportunities to simplify, automate and strengthen digital platform delivery.
• Develop capability in MLOps practices such as experiment tracking, model packaging, model registry, model promotion and governed deployment workflows.
• Support AI developers with repeatable workflows that move ML code, configuration and artefacts through controlled development, test, QA and production stages.
• Contribute to the longer-term development of the ML platform, including quality gates, lifecycle controls, operational monitoring and supportability.

Skills

DevOps

Cloud operations

CI/CD pipelines

Azure cloud services

IoT data platforms

Python scripting

Automation

Infrastructure as code

Monitoring & observability

ML/AI platform awareness

Education

BSc/MSc CS or related

Equivalent experience

Tools

GitHub

Azure DevOps

Azure IoT

Databricks

Azure Data Explorer

CI/CD tooling

Job description

Job Title: DevOps & IoT Platform Engineer

Location: Redruth, Cornwall UK

Location Type: Hybrid (2/3)

Website: https://www.wmfts.com/en/

Group: https://www.spiraxgroup.com/

Watson-Marlow Fluid Technology Solutions is part of Spirax Group, a FTSE100 and FTSE4Good multi-national industrial engineering Group with expertise in the control and management of steam, electric thermal solutions, peristaltic pumping and associated fluid technologies.

When you join us, you will be integrated into a cooperative and encouraging team, participate in challenging yet critical work, and experience ongoing growth opportunities to help you achieve your full potential. Visit our website to learn more.

Job Summary:

The DevOps & IoT Platform Engineer is responsible for supporting the build, operation, automation, and reliability of the digital infrastructure that underpins Watson-Marlow’s connected products and industrial IoT capabilities.

The role will initially focus on DevOps, cloud platform operations, IoT data flows, deployment automation, environment management, monitoring, and secure integration between connected devices, cloud services, data platforms, and engineering teams.

As the capability matures, the role will provide a clear development path into MLOps and ML platform engineering, supporting model deployment, model lifecycle controls, ML pipeline automation, and governed promotion of machine learning solutions from development into production.

Key Responsibilities:

DevOps, cloud operations and platform reliability

• Build, maintain and improve automation for cloud and IoT platform services that support connected products.
• Develop and maintain CI/CD pipelines, deployment workflows, environment controls and repeatable release processes using tools such as GitHub, Azure DevOps and related platform tooling.
• Help maintain Azure-based development, test, QA and production environments used by digital, IoT, data and ML platforms.
• Contribute to secure platform configuration, access control, secrets management, monitoring and operational governance.
• Monitor platform health, investigate operational issues and contribute to improving reliability, supportability and repeatability across digital product environments.

IoT data flows, data pipelines and operational support

• Support reliable device-to-cloud data flows from connected products, gateways, cloud services and downstream data platforms.
• Work with telemetry, time-series and industrial IoT data sources, helping to ensure data is available, structured and usable for engineering, product and analytics teams.
• Support operational data pipelines that provide reliable data for engineering analytics, product insights, connected services and machine learning use cases.
• Assist with troubleshooting data ingestion, connectivity, data quality and integration issues across the connected-products ecosystem.
• Document deployment steps, support processes and platform knowledge to improve maintainability and reduce reliance on manual intervention.

Collaboration, security and controlled platform change

• Work with IT, cyber security, software, firmware and supplier teams to support service availability, incident resolution and controlled platform change.
• Contribute to data validation, monitoring, transformation and handover between operational platforms and analytical environments.
• Proactively identify opportunities to simplify, automate and strengthen digital platform delivery.

Growth into MLOps and ML platform engineering

• Develop capability in MLOps practices such as experiment tracking, model packaging, model registry, model promotion and governed deployment workflows.
• Support AI developers with repeatable workflows that move ML code, configuration and artefacts through controlled development, test, QA and production stages.
• Contribute to the longer-term development of the ML platform, including quality gates, lifecycle controls, operational monitoring and supportability.

Skills/Experience:

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Data Science, Software Engineering, or a related discipline (or equivalent experience)

Skills & Experience

• Experience in DevOps, platform engineering, cloud operations, software deployment, or infrastructure automation
• Experience with CI/CD pipelines, source control, branching strategies, pull requests, and release workflows using GitHub, Azure DevOps, or similar tools
• Exposure to Azure cloud services and operational practices, including monitoring, access control, environment management, and secure configuration
• Experience working with IoT, telemetry, time-series, connected-product, or industrial data platforms
• Proficiency in scripting or automation using Python, PowerShell, Bash, or equivalent tooling
• Understanding of data pipelines, APIs, integration patterns, and operational troubleshooting
• Strong problem-solving skills with the ability to work across software, data, cloud, and connected-product teams
• Experience with Azure IoT services, Azure Data Explorer, Databricks, containerisation, APIs, or cloud-native application deployment
• Experience with infrastructure as code, automated testing, observability, logging, or platform monitoring
• Awareness of MLOps concepts such as MLflow, model registry, model promotion, model monitoring, and governed deployment pipelines
• Exposure to machine learning workflows or data science environments, with an interest in developing into ML platform engineering
• Experience working with industrial equipment, embedded systems, firmware teams, or device-to-cloud architectures