Lead Enterprise Software Engineer

We are seeking a talented, collaborative, high-energy engineering professional with extensive technical expertise and a passion for automating infrastructure and deployment processes to help build, test, and release the next generation of our cloud-based AI-enabled Expert Solutions as part of the LR AppOps Service Operations & Delivery Team.
The Team provides the DevOps and SRE services which are critical to Wolters Kluwer and our Expert Solutions in the Legal & Regulatory division.
As a Lead DevOps Engineer, you will be responsible for leading the design and implementation of the infrastructure and deployment pipelines for our applications, as well as maintaining and improving reliability of our existing systems. You will work alongside the Wolters Kluwer Product Teams to successfully implementing and bring any learnings through continuous improvements. You will work closely with the Application Development Teams to ensure that our services and solutions are delivered efficiently and with the highest level of quality. In addition, you will be responsible for mentoring a team of DevOps engineers, fostering a culture of collaboration with continuous improvement and learning.
Essential Duties And Responsibilities : Design, implement, and automate cloud infrastructure across Azure and AWS using Infrastructure as Code (Terraform, Ansible), enabling scalable, secure, and repeatable environments
Drive and scale the adoption of AI capabilities across the portfolio, enabling Teams to leverage AI-powered services to improve productivity, operational efficiency, and service reliability, while ensuring alignment with enterprise security, data protection, and governance standards
Lead the adoption of DevSecOps and Infrastructure as Code practices across Service Operations, Delivery, and the wider organisation
Drive the design and evolution of containerised and serverless architectures (AKS, EKS, Docker, Azure Functions)
Define, build, and maintain CI/CD pipelines to improve deployment frequency, reliability, and lead time
Implement observability standards, including SLIs/SLOs, monitoring, logging, and alerting to ensure proactive issue detection and reduced MTTR
Apply SRE principles to improve resilience, scalability, and reduce operational TOILs
Ensure solutions align with enterprise DevOps, security, and compliance standards
Embed security-by-design across the SDLC, including vulnerability management and secure configuration practices
Mentor and lead DevOps engineers, fostering a high-performing and learning-oriented culture
Act as a technical authority, contributing to architecture decisions and engineering standards
Enable platform capabilities and reusable automation patterns to support scalable delivery across Teams
Lead incident resolution, root cause analysis, and continuous improvement initiatives
Drive continuous improvement by identifying engineering inefficiencies and proposing modernisation strategies
Job Qualifications : Hold at least two Azure &/or AWS Certifications
Education : Bachelor's in Engineering or Master's degree in Computer Science
Required Experience : 10+ years (or equivalent senior experience) in DevOps, SRE, Release Engineering, or Software Engineering, with exposure to production-critical systems
Strong hands‑on experience with cloud platforms (Azure and/or AWS), including designing and operating scalable and secure solutions
Proven experience in maintaining and deploying highly available, fault-tolerant cloud-native systems at scale using Infrastructure as Code automation, particularly Terraform (preferred) or CloudFormation
Experience using AI-assisted tools (e.g. GitHub Copilot, ChatGPT, or similar) to improve productivity
Experience automating operational tasks using AI-assisted scripting or intelligent automation approaches
Familiarity with prompt engineering and AI usage patterns in engineering workflows
Solid experience building and managing CI/CD pipelines, using tools such as Azure DevOps, Jenkins, GitHub
Experience building or operating AI-enabled pipelines (e.g. integrating ML/AI services into CI/CD workflows)
Strong scripting/automation skills using Python, Bash or PowerShell
Experience working in both Linux and Windows environments, including troubleshooting and automation
Solid Experience with Kubernetes (AKS/EKS) and containerised environments
Experience with observability practices, including Monitoring, Logging, Alerting (e.g. Datadog, Prometheus, Grafana, CloudWatch)
Practical application of SRE principles, including Incident Management, Relaiability and Resilience, Toil Reduction
Experience applying DORA metrics to improve delivery performances
Familiarity with enterprise tooling ecosystem (e.g. ServiceNow)
Proven ability to lead or mentor engineers and work across multiple Teams
Strong communication skills, with the ability to explain complex technical topics