DevOps & IoT Platform Engineer: Cloud, Data & ML

Watson-Marlow Fluid Technology Solutions

Redruth

Hybrid

GBP 65,000 - 90,000

Full time

12 days ago

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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.