DevOps Engineer at CliffordChance

The role  This role is a senior key contributor in the overall Legal Technology solutions and Data and Analytics, AI initiatives of the firm. You will be automating the environments for Data Science Lab applications in Azure Cloud and Clifford Chance on-premises. You will help integrate third-party applications (such as ContraxSuite) and AI/LLM workloads on Azure Cloud, collaborating closely with LTS stakeholders, data science lab, infrastructure, operations, and security teams. Who you will work with The candidate will be responsible for implementing and maintaining all on-premises and Azure infrastructure used by the Data and Analytics, AI team. You will help Data Scientists on automation-related challenges. It is expected that you will work in autonomy, implementing best practices (including Microsoft Gold standards), having high appreciation for security-related challenges, and working in a fully automated environment (everything must be scripted).   Skill & Key Responsibilities Scripting & Automation • Expert-level proficiency in scripting for automation using PowerShell, Bash, and Python. • Design and develop automation templates/tools for infrastructure provisioning, configuration, and change management with a strong focus on Infrastructure as Code (IaC):  ARM Templates, Azure Blueprints, Terraform, BicepsAzure Infrastructure: • Deploy, configure, and troubleshoot Azure components: • Networking: NSGs, Subnets, Firewalls, Application Load Balancers (ALBs), App Gateway, Certificates. • Compute & Services: Azure App Service, Virtual Machines, Scale Sets, AKS. • Managed Services: SQL Server, PostgreSQL, Azure ASE. • Storage: Storage Accounts, Blob, Data Lakes. • Working knowledge of data services: Azure Databricks, Azure Data Factory, Azure Insights, Azure AI Services. • Expertise in Logging, Monitoring, Azure AD, and Azure Security Center.DevOps Practices: • Hands-on experience with: • CI/CD Pipelines: Azure Pipelines, GitHub Actions. • Source Control: Azure Repos, GitHub. • Build & Test Automation, Configuration Management, Containerization, Deployment Automation, Log Management, Performance Monitoring, and Transaction Analytics. • Develop scripts/tools for CI/CD of containerized applications.AI/ML Operations: • LLMOps: • Manage inference pipelines, token usage optimization, and cost-aware deployments of GPT/Azure OpenAI models. • Deploy and monitor LLM-powered microservices. • Tools: Azure OpenAI, Azure Prompt Flow, MLflow, Weights & Biases. • MLOps & Model Lifecycle: • End-to-end lifecycle management: experimentation → deployment → monitoring. • Automated CI/CD for model artifacts using Azure ML, MLflow registry, and A/B testing. • GPU-based cluster provisioning and cost optimization for inference.Kubernetes & Container Security: • Expertise in managing, securing, and automating AKS clusters, including: • Helm, Autoscaling, Service Mesh, RBAC, GitOps practices. • Implement security best practices for containerized environments: • Image scanning, runtime protection, vulnerability management. • Familiarity with cybersecurity risks and mitigation strategies.  Additional Responsibilities: • Assess current environments and recommend improvements for performance and scalability. • Evaluate emerging cloud and storage technologies. • Assist in design, architecture, and implementation of cloud solutions and deployment strategies. • Troubleshoot and escalate issues as needed. • Collaborate effectively with cross-functional teams in an Agile delivery environment. • Optimize Azure costs using tools like Azure Cost Management, Advisor recommendations, and implement cost-saving measures (reserved instances, rightsizing, scaling automation). • Knowledge of Azure Active Directory, role assignments, and conditional access policies. • Design and deploy event-driven architectures using Azure Queue and Service Bus. • Experience with Azure AI services, Vector Databases (e.g., Azure Cognitive Search). • Automate deployment of AI/ML models in production using Azure ML and related tools.Technical Stack • Languages: Python, Node.js, SQL.Certifications (Preferred) • Azure Solutions Architect Expert • Azure Administrator Associate • Azure DevOps Engineer Expert (or equivalent)