Digital Lab - Cloud Engineer

Job description

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: • 4–7 years of experience in DevOps, Cloud Engineering, SRE, Platform Engineering, or Infrastructure Automation • Hands-on experience working with AWS cloud services in development, test, or production environments • Good knowledge of Infrastructure as Code using Terraform, CloudFormation, or AWS CDK • Experience with CI/CD tools such as GitHub Actions, GitLab CI, Azure DevOps, or Jenkins • Hands-on experience with Docker and exposure to container orchestration platforms such as EKS, ECS, or Kubernetes • Familiarity with AWS services such as EC2, S3, IAM, VPC, CloudWatch, Lambda, RDS, Route 53, and Load Balancers • Understanding of Linux administration, scripting using Python, Bash, or Shell, and version control using Git • Knowledge of basic cloud security practices including IAM, encryption, secrets handling, and vulnerability management • Experience with monitoring, logging, and troubleshooting in cloud environments • Practical experience using current AI tools in the market or AI-assisted engineering tools to improve productivity in coding, scripting, automation, troubleshooting, or documentation • Ability to validate AI-generated outputs and use them responsibly in an engineering environment • Good communication, collaboration, and documentation skillsDesirable Skills/Experience: • Experience supporting AWS migration or cloud transformation projects • Exposure to EKS, Kubernetes, Helm, ArgoCD, or Flux • Familiarity with serverless services such as Lambda, EventBridge, Step Functions, or API Gateway • Exposure to observability tools such as Prometheus, Grafana, ELK/OpenSearch, or OpenTelemetry • Understanding of policy-as-code, security scanning, or compliance tooling such as Checkov, OPA, Security Hub, GuardDuty, or Inspector • Experience in regulated environments or teams with formal release and change management practices • Familiarity with FinOps concepts such as tagging, budgeting, rightsizing, and cost allocation • Experience using tools such as GitHub Copilot, Amazon Q, ChatGPT Enterprise, Claude, Cursor, or equivalent enterprise-approved AI tools • Exposure to supporting data or AI/ML workloads on cloud platforms is a plus, but not required • At least one relevant certification is preferred, such as: AWS Certified Solutions Architect –