Devops Engineer

Devops Engineer - Detalhes da Vaga. ● You will also play a key role in embedding AI-driven practices into DevOps, enabling faster and more reliable delivery of data and AI products. ●You will work closely with data engineers, platform teams, and product teams to ensure seamless, scalable, and intelligent deployment processes across the Anglo American Data Platform. ●Key ResponsibilitiesDevOps Strategy and Platform Evolution

• Define and implement a modern DevOps and platform engineering strategy aligned with data and AI platform goals. ●
• Develop roadmaps that incorporate AI-assisted development, testing, and operations. ●
• Drive the evolution from traditional DevOps to intelligent, self-service platform capabilities. ●
• Continuously evaluate emerging technologies (e. ●G. ●, GenAI, LLMOps, AIOps) and incorporate them where relevant. ●AI-Enabled CI/CD and Automation
• Design and optimize CI/CD pipelines using AI-assisted tools (e. ●G. ●, code generation, test generation, pipeline optimization). ●
• Integrate AI copilots and automation agents into development and deployment workflows. ●
• Implement intelligent quality gates (e. ●G. ●, automated code reviews, anomaly detection in pipelines). ●
• Enable self-healing pipelines and automated failure diagnostics where possible. ●Automation and Framework Enhancement
• Build scalable automation frameworks leveraging AI, scripting, and infrastructure as code. ●
• Automate repetitive tasks using AI agents, prompt-based workflows, or orchestration frameworks. ●
• Enhance DevOps pipelines to support data products and AI/ML workloads (MLOps/LLMOps). ●
• Standardize reusable templates and pipeline components for platform-wide adoption. ●Data & AI Platform IntegrationAnalyze and optimize integrations across the Anglo American Data Platform, including: Databricks (data processing, workflows, DABs)Airflow (orchestration)Azure services (compute, storage, identity)Power BI / downstream consumption layersSupport deployment patterns for AI/ML models, feature pipelines, and inference services. ●Enable end-to-end lifecycle management for AI applications (training deployment monitoring). ●Governance, Security, and Reliability
• Implement governance practices across pipelines, including policy-as-code and automated compliance checks. ●
• Manage access control and ensure secure DevOps practices across environments. ●
• Introduce AIOps practices for monitoring, alerting, and incident management. ●
• Ensure high availability, scalability, and observability of DevOps processes. ●Documentation and Developer Experience
• Create and maintain clear documentation, including AI-assisted "how-to" guides and self-service enablement. ●
• Improve developer experience through intelligent tooling, chat-based interfaces, and automation. ●
• Promote adoption of DevOps and AI capabilities across teams. ●Troubleshooting and Operational Support
• Collaborate with Data Delivery and platform teams to resolve issues efficiently. ●
• Use AI-assisted diagnostics and root cause analysis tools to accelerate incident resolution. ●
• Support production environments and ensure stability of pipelines and deployments. ●Standards and Best Practices
• Define and promote best practices in DevOps, platform engineering, and AI-enabled delivery. ●
• Coach teams on adopting modern DevOps + AI approaches. ●
• Drive consistency and reuse across teams and projects. ●Required Skills and QualificationsTechnical Expertise
• Strong experience with CI/CD tools (e. ●G. ●, Azure DevOps, GitHub Actions). ●
• Expertise in infrastructure as code (Bicep, ARM or similar). ●
• Proficiency in scripting (PowerShell, Python, Bash). ●
• Deep understanding of DevOps principles, Git workflows, and release strategies. ●
• Experience with Azure services and cloud-native architectures. ●
• Familiarity with data platforms (Databricks, ADF, Airflow, SQL, AAS or equivalent). ●AI & Modern DevOps Capabilities
• Hands-on experience or strong familiarity with: AI-assisted development tools (e. ●G. ●, GitHub Copilot, ChatGPT, code assistants)
• MLOps / LLMOps concepts (model deployment, monitoring, versioning)
• AIOps tools for monitoring and incident management
• Understanding of how AI can be applied to: Code generation and testing
• Pipeline optimization
• Incident detection and resolution
• Experience integrating APIs or services for AI capabilities into workflows is a plus. ●Platform & Systems Knowledge
• Experience with Azure cloud platform
• Knowledge of data and AI workload deployment patterns. ●
• Understanding of observability tools and practices. ●Problem-Solving and Analytical Skills
• Strong ability to analyze complex systems and improve scalability and performance. ●
• Proven troubleshooting skills in DevOps and platform environments. ●Collaboration and Communication
• Ability to work across technical and business teams. ●
• Strong communication skills, including documenting and explaining complex concepts. ●
• Experience enabling teams through tooling and best practices. ●Governance and Standards
• Experience with governance frameworks, access control, and compliance. ●
• Ability to implement and enforce DevOps standards at scale. ●Preferred Qualifications
• Azure DevOps Engineer certification or equivalent. ●
• Experience in enterprise-scale DevOps or platform engineering environments. ●
• Exposure to data platforms and AI-driven use cases in production. ●
• Experience with agent-based automation or orchestration frameworks is a plus. ●. Requisitos para participar do processo. ● . O que oferecemos.