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GCL: C2 Introduction to role: Are you ready to harness the power of AWS and automation to accelerate scientific breakthroughs and bring medicines to patients faster Join a high-impact team that builds secure, scalable cloud platforms enabling our labs and product teams to move from idea to value at speed. In this role, you will design and automate cloud infrastructure that underpins critical digital capabilities. Working side-by-side with engineers, security specialists, data teams, and platform owners, you will raise deployment velocity, strengthen reliability, and unlock developer productivity. You will use modern engineering tools improved by artificial intelligence responsibly to amplify your impact and keep our platforms robust and compliant. Can you see yourself turning complex requirements into resilient, automated solutions that scale across a global enterprise Accountabilities: Cloud Infrastructure: Design, implement, and maintain AWS compute, storage, networking, and identity services that deliver secure, scalable foundations for digital and lab applications. Build reusable Terraform, CloudFormation, and CDK modules to standardize environments, reduce lead times, and improve quality. Use AI-assisted tools to improve scripting and documentation while maintaining detailed reviews. CI/CD and Automation: Create and evolve pipelines in GitHub Actions, GitLab CI, Jenkins, or similar to automate build, test, security scanning, and deployment, driving consistent, auditable releases. Containers and Platform Support: Deploy and operate Docker-based services on EKS, ECS, or Fargate, improving availability, release consistency, and operational efficiency for product teams. Observability and Operations: Implement end-to-end tracking, recording, and alerting with CloudWatch, Prometheus, Grafana, OpenTelemetry, or OpenSearch; lead incident analysis and service improvement to reduce MTTR and prevent recurrence. Security and Compliance: Embed identity and access controls, data protection, confidential information handling, vulnerability remediation, patching, and network controls into every layer of the stack; ensure AI tools are used responsibly and in line with enterprise policies. Networking: Configure and optimize VPCs, subnets, route tables, NAT, security groups, load balancers, and DNS with guidance from senior engineers, supporting reliable connectivity and performance. Migration and Modernization: Contribute to on-prem to AWS transition and projects focused on improving processes, accelerating environment setup, deployment automation, testing, and stabilization to de-risk cutovers. AI-Enabled Engineering: Use enterprise-approved AI assistants to speed coding, infrastructure automation, troubleshooting, and documentation; validate outputs and uphold standards to ensure accuracy and compliance. Cost Optimization: Implement tagging, monitor consumption, identify waste, and support rightsizing to reduce spend without sacrificing performance. Documentation and Collaboration: Produce clear runbooks and implementation notes; engage developers, architects, security, and operations to align on designs and drive continuous improvement. Value and Impact Progression: Deliver quick wins by stabilizing and automating priority services; then scale patterns, playbooks, and modules across teams to raise reliability and throughput enterprise-wide. Essential Skills/Experience:47 years of experience in DevOps, Cloud Engineering, SRE, Platform Engineering, or Infrastructure AutomationHands-on experience working with AWS cloud services in development, test, or production environmentsGood knowledge of Infrastructure as Code using Terraform, CloudFormation, or AWS CDKExperience with CI/CD tools such as GitHub Actions, GitLab CI, Azure DevOps, or JenkinsHands-on experience with Docker and exposure to container orchestration platforms such as EKS, ECS, or KubernetesFamiliarity with AWS services such as EC2, S3, IAM, VPC, CloudWatch, Lambda, RDS, Route 53, and Load BalancersUnderstanding of Linux administration, scripting using Python, Bash, or Shell, and version control using GitKnowledge of basic cloud security practices including IAM, encryption, secrets handling, and vulnerability managementExperience with monitoring, logging, and troubleshooting in cloud environmentsPractical experience using current AI tools in the market or AI-assisted engineering tools to improve productivity in coding, scripting, automation, troubleshooting, or documentationAbility to validate AI-generated outputs and use them responsibly in an engineering environmentGood communication, collaboration, and documentation skillsDesirable Skills/Experience:Experience supporting AWS migration or cloud transformation projectsExposure to EKS, Kubernetes, Helm, ArgoCD, or FluxFamiliarity with serverless services such as Lambda, EventBridge, Step Functions, or API GatewayExposure to observability tools such as Prometheus, Grafana, ELK/OpenSearch .
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.