Senior Cloud Security Engineer
Skills
Computer ScienceCloud SecurityDevsecopsIdentity And Access ManagementInformation SecurityMicrosoft SurfaceModeling
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- Bachelor’s degree or equivalent in Computer Science Information Security or related field
- Experience designing and securing cloud and hybrid environments across AWS Azure GCP and on-premises
- Proficiency in one or more programming or scripting languages and cloud APIs
- Strong understanding of IAM network security encryption and secure architecture design
- Experience implementing DevSecOps practices and securing Infrastructure-as-Code workflows
- Experience deploying and securing Kubernetes Docker EKS GKE or AKS
- Understanding of AI ML security risks including prompt injection data poisoning and model or data leakage
- Familiarity with data security principles in AI training and inference pipelines
- Experience implementing security controls logging and monitoring for AI enabled services
- Awareness of AI security frameworks such as OWASP Top 10 for LLMs and NIST AI RMF
- Experience using CNAPP platforms to identify and remediate cloud security risks
- Familiarity with IaC scanning cloud security posture management and runtime detection tools
- Understanding of security prevention detection and response concepts
- Experience building and securing scalable cloud architectures across application network and data layers
- Familiarity with serverless and event-driven architectures
- Relevant certifications such as AWS Azure GCP or Security+ are a plus
- Experience working in multi-OS and distributed environments
Nice to have
- Inspirational
- Hardworking
- Collaborative
- Analytical
- Detail-oriented
Day to day
- Design and implement proactive cloud security controls that reduce risk and strengthen prevention detection and response across modern environments
- Secure multi-cloud and hybrid platforms spanning AWS Azure GCP and on-premises infrastructure using strong engineering practices
- Protect AI ML workloads containerized systems and cloud-native services through automation secure architecture patterns and continuous monitoring
Hiring process
- Background checks at offer stage