Lead DevOps Engineer (Data & AI Platform)

Are you ready to join a cutting-edge Digital Solutions company and help shape the future of enterprise software

Our client is advancing a world-class data and AI platform that powers decision-making across the entire mining value chain. Built on Azure and Databricks thrir platform enables scalable data products advanced analytics and emerging AI capabilitiesfrom digital twins to intelligent automation and natural language querying.

We are looking for a Lead DevOps Engineer to play a critical role in evolving the platform into a highly automated AI-enabled delivery ecosystem. This role goes beyond traditional DevOpsfocusing on platform engineering AI-assisted development and intelligent automation of software delivery and operations. You will lead the design and implementation of modern DevOps practices integrating AI tools copilots and automation frameworks to significantly improve developer productivity pipeline efficiency and platform reliability.

As a Lead DevOps Engineer you will refine and enhance CI/CD processes integrate new capabilities into release pipelines and drive automation across the software delivery lifecycle. 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.

Essential Functions:

DevOps 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 Integration

• Analyze and optimize integrations across the Anglo American Data Platform including:
  o Databricks (data processing workflows DABs)
  o Airflow (orchestration)
  o Azure services (compute storage identity)
  o Power BI / downstream consumption layers

• Support 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 selfservice 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 Qualifications:

Technical 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:
  o AI-assisted development tools (e.g. GitHub Copilot ChatGPT code assistants)
  o MLOps / LLMOps concepts (model deployment monitoring versioning)
  o AIOps tools for monitoring and incident management

• Understanding of how AI can be applied to:
  o Code generation and testing
  o Pipeline optimization
  o 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.