DevOps/Infrastructure Engineer

At Freshfields, technology and innovation are embedded into how we work with our clients. We’re a global law firm with more than 5,500 people across 33 offices, and we are continuing to advance our innovation strategy including investment in key technology. Our partnerships with Anthropic and Google as well as the investment in Freshfields Lab and AI Academy are but a few examples. Join us to work on some of the most exciting challenges at the intersection of law, technology and business. Responsibilities Design, build, deploy, and support secure, scalable and resilient AI and cloud-based solutions on Azure. Engineer and maintain AI application infrastructure, including Azure Kubernetes Service (AKS), containers, and cloud-native services. Develop and manage Infrastructure as Code (Terraform) to provision and maintain secure, reliable environments. Build, optimize, and maintain CI/CD pipelines for AI, data, and application deployment using Azure DevOps. Implement automated testing, monitoring, alerting, and observability capabilities to ensure platform reliability and performance. Automate operational processes, configuration management, and environment provisioning using modern DevOps practices and tools. Collaborate with product, data, and engineering teams to transition new AI solutions into production environments. Monitor platform health, troubleshoot incidents, and drive root cause resolution and continuous improvement. Ensure all solutions are documented, secure, supportable, and aligned with engineering standards and best practices. Key Experience & Skills 5+ years of experience designing and implementing cloud-native solutions, preferably on Microsoft Azure. Strong hands-on experience with Kubernetes (AKS), Docker, and containerized application deployment. Expertise in Infrastructure as Code using Terraform and related automation frameworks. Experience building and maintaining CI/CD pipelines with Azure DevOps and YAML-based workflows. Strong programming and scripting skills in Python, plus shell scripting (PowerShell or Bash); C# or Go a plus. Experience deploying and supporting AI, machine learning, API, and cloud application workloads in production environments. Experience operating LLM-based workloads in production, including multi-provider routing, capacity and quota management, rate limiting, streaming and asynchronous job patterns, retries and backpressure, cost attribution, and AI-specific tracing and observability. Strong understanding of Linux administration, networking, security, and cloud architecture principles. Experience with enterprise Azure security patterns, including private networking and private endpoints, Entra ID and managed identities, RBAC, and secrets management with Key Vault. Experience operating PostgreSQL, Redis, Azure Service Bus and Azure storage services in production.. Proficiency with Git, automated testing frameworks, and modern software engineering…