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Who are we? We are HCLTech , one of the fastest-growing large tech companies in the world and home to 225,000+ people across 60 countries, supercharging progress through industry-leading capabilities centered around Digital, Engineering and Cloud. The driving force behind that work, our people, are diverse, creative, and passionate, raising the bar for excellence on a regular basis. We, in turn, work hard to bring out the best in them as we strive to help them find their spark and become the best version of themselves that they can be. If all this sounds like an environment you’ll thrive in, then you’re in the right place. Join us on our journey in advancing the technological world through innovation and creativity. About the Role 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, the platform enables scalable data products, advanced analytics, and emerging AI capabilities—from 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 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. Role Overview 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. You will work closely with data engineers, platform teams, and product teams to ensure seamless, scalable, and intelligent deployment processes across the client's Data Platform. 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. 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 client's 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 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). 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. 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. HCLTech is committed to protecting and securing the privacy and confidentiality of the Personal Data which it collects directly or indirectly from you when applying for a job at HCLTech either directly or through a third-party human resources agency. This notice (the “Notice”) outlines and explains how HCL Technologies Limited including its subsidiaries, local employing entities, associates, and affiliated companies [collectively referred to as “HCLTech”, “us,” “our”, or “we”] will process your Personal Data in accordance with applicable privacy legislation(s).
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.