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We are seeking an experienced (5+ years), motivated, and hands-on Cloud Platform DevOps Engineer to join our North American AI and DevOps Platform Engineering team. In this critical role, you will be responsible for enhancing the stability, reliability, and performance of our AI and DevOps platforms, which support a diverse ecosystem of AI applications, developer tools, and CI/CD pipeline technologies across the organization. You will actively contribute to infrastructure design, implementation, and maintenance, and facilitate agile development within the team. The ideal candidate is a strong technical leader who champions agile practices, drives continuous improvement, and excels in both coding and coaching, possessing a deep understanding of infrastructure and operational considerations for Artificial Intelligence and Machine Learning initiatives, with proven hands-on experience in DevOps tools and technologies such as Kubernetes, Docker, HELM, Ansible, DevOps tools, or similar CI/CD platforms, and proficiency in scripting and automation (e.g., Python, Bash). We are looking for someone with a track record of implementing scalable, resilient, and high-performance solutions, coupled with strong communication and collaboration skills, and an ability to mentor and guide junior team members, as you join a dynamic team committed to fostering innovation and collaboration. Responsibilities: Hands-on DevOps & Infrastructure Engineering Design & Implementation: Lead the design, implementation, and ongoing management of secure, scalable, and resilient infrastructure components. Secret & Certificate Management: Administer and maintain secret and certificate management solutions using HashiCorp Vault, including policy definition and integration. Database Management: Perform hands-on administration and optimization of database systems (PostgreSQL, Oracle, MongoDB), including performance tuning, backup, and recovery strategies. Workflow Orchestration: Deploy, monitor, and troubleshoot data orchestration workflows using Apache Airflow, and develop/optimize DAGs. Messaging Systems: Implement and manage messaging queues such as Kafka and IBM MQ, including cluster setup and configuration. API Integrations: Develop, maintain, and troubleshoot RESTful API and SOAP integrations critical for system connectivity. Build Automation: Implement and optimize build and deployment processes using Gradle. Container Orchestration: Design, implement, and manage container orchestration platforms with Kubernetes and Helm, including integration with CyberArk and HashiCorp for secrets management. Create, debug, and troubleshoot Kubernetes PODs, Jobs, and Deployments using YAML. Storage Management: Configure and manage persistent storage solutions including PVC, SONiC NAS, and S3, with an awareness of storage requirements for AI/ML workloads. Networking & Load Balancing: Set up and maintain load balancing solutions (e.g., Nginx, HAProxy, AWS ELB/ALB, Kubernetes Ingress controllers) for high availability and performance. Monitoring & Logging: Implement, configure, and utilize comprehensive monitoring and logging solutions (Prometheus, Grafana, ELK Stack) to ensure system health and proactively identify issues, including those relevant to AI/ML applications. Automation & Scripting: Develop robust automation scripts and tools using Python, Bash, Go, or similar languages to streamline operations and enhance efficiency. Incident Response: Participate actively in on-call rotations, responding to and resolving critical incidents with hands-on troubleshooting. Documentation: Create and maintain technical documentation, architecture diagrams, and runbooks for infrastructure components and processes. Impediment Resolution: Proactively identify and resolve technical impediments and process bottlenecks within the team and across organizational boundaries, paying special attention to unique challenges posed by AI/ML infrastructure. Backlog Refinement: Collaborate closely with stakeholders (e.g., product owners, technical leads) to ensure a well-defined and prioritized backlog for infrastructure work, technical debt, operational improvements, and AI/ML platform needs. Process Improvement: Drive continuous improvement in the team's agile and DevOps practices, helping them adapt and optimize their workflow for maximum efficiency and quality. Required Qualifications: Hands-on DevOps & Infrastructure Engineering Expertise Secret & Certificate Management: Proven hands-on experience with HashiCorp Vault (installation, configuration, policy management, integrations). Database Administration: Strong hands-on experience with at least two of PostgreSQL, Oracle, or MongoDB (installation, tuning, replication, backup/restore). Workflow Orchestration: Hands-on experience deploying, managing, and developing DAGs for Apache Airflow. Messaging Systems: Solid hands-on experience with Kafka and/or IBM MQ (cluster setup, topic management, producer/consumer configuration). Container Orchestration: In-depth hands-on experience with Kubernetes and Helm, including YAML configuration, troubleshooting PODs/Jobs/Deployments, and integrations with secrets management (CyberArk, HashiCorp). Storage Management: Practical experience with Kubernetes PVCs, Persistent Volumes, S3, and/or enterprise NAS solutions (e.g., SONiC NAS). Monitoring & Logging: Strong hands-on experience with Prometheus, Grafana, and the ELK Stack (setup, dashboard creation, query optimization, alert configuration). Scripting & Automation: High proficiency in Python, Bash, or Go for automation, tooling development, and system administration. Cloud Platforms: Extensive hands-on experience with at least one major cloud provider (AWS, Azure, GCP). Infrastructure as Code (IaC): Proficiency with IaC tools such as Terraform or Ansible. CI/CD: Experience designing, implementing, and maintaining CI/CD pipelines (e.g., Jenkins, GitLab CI, GitHub Actions). API Integration: Experience with RESTful API and SOAP web services. Build Tools: Proficiency with Gradle for build automation. AI/ML Awareness & Support AI/ML Infrastructure Concepts: Understanding of the specific infrastructure requirements for deploying, managing, and scaling Artificial Intelligence and Machine Learning workloads (e.g., GPU resources, specialized storage, MLOps pipelines). Data for AI/ML: Awareness of data management strategies and data governance principles relevant to AI/ML models and training datasets. Monitoring AI/ML Systems: Familiarity with metrics and monitoring approaches for the performance and health of AI/ML applications and their underlying infrastructure. Agile & Leadership Skills Working Scrum Master Experience: Proven experience acting as a Scrum Master within a technical team where you also performed significant hands-on engineering. Agile & Scrum Mastery: In-depth knowledge and practical application of Agile principles and the Scrum framework. Facilitation & Coaching: Excellent facilitation, coaching, and mentoring skills within a technical context. Communication: Strong verbal and written communication skills, able to bridge technical and process discussions. Technical Leadership: Ability to guide technical discussions, influence architectural decisions, and drive best practices. Preferred Qualifications: Certified ScrumMaster (CSM) or Professional Scrum Master (PSM) certification. Relevant cloud certifications (e.g., AWS Certified DevOps Engineer, Azure DevOps Engineer Expert, GCP Professional Cloud DevOps Engineer). Experience with site reliability engineering (SRE) principles and practices. Familiarity with other Agile scaling frameworks (e.g., SAFe, LeSS). Exposure to MLOps platforms or tools (e.g., Kubeflow, MLflow). Education: Bachelor's or Master's degree in computer science, Engineering, or a related technical field or equivalent experience ------------------------------------------------------ Job Fam
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.