Lead DevOps Engineer
Descrierea postului
Who we are?
HCL Technologies is a next-generation global technology company that helps enterprises reimagine their businesses for the digital age. Our technology products, services, and engineering are built on four decades of innovation, with a world-renowned management philosophy, a strong culture of invention and risk-taking, and a relentless focus on customer relationships.
We are looking for a Lead DevOps Engineer to play a critical role in evolving our platform into a highly automated, AI-enabled delivery ecosystem. This role goes beyond traditional DevOps—focusing 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.
Key Responsibilities
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.
- 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.
- Analyze 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 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).
- 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 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).
- 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.
- Experience with Azure cloud platform
- Knowledge of data and AI workload deployment patterns.
- Understanding of observability tools and practices.
- Strong ability to analyze complex systems and improve scalability and performance.
- Proven troubleshooting skills in DevOps and platform environments.
- We are investing in your growth, offering learning and career development opportunities at every level to help you find your spark;
- We offer a virtual-first work environment, promoting a good work-life balance and real flexibility; • Our company is extremely diverse with representation of 165 nationalities;
- We offer the opportunity to work with colleagues across the globe;
- We offer comprehensive benefits for all employees;
- We are a certified great place to work and a top employer in 25 countries including Romania, offering a positive work environment that values employee recognition and respect.
Te interesează acest rol?