Senior AI & Cloud ,Devops, Security Engineer

We are looking for a senior hands-on security engineer to secure AI applications, cloud platforms, and the engineering pipelines that connect them.

This role combines AI security, cloud security, application security, and DevSecOps, with a strong focus on LLM, RAG, and agentic workloads. You will work directly with engineering teams to identify risks, design practical controls, automate security checks, and help AI solutions move safely into production.

What you'll do

Threat-model and security-review LLM applications, RAG architectures, and agentic workflows, including risks such as prompt injection, data leakage, insecure tool use, and excessive agency

Design and execute AI security testing and adversarial evaluations, working with specialist red-team teams where appropriate

Secure AI and data supply chains, including model and artifact provenance, dependencies, vector stores, grounding data, and third-party integrations

Embed security into CI/CD through policy-as-code, automated security testing, container and IaC scanning, and vulnerability management

Design and implement cloud security controls across identity,network segmentation, secrets and key management, data protection, logging, and auditability

Build security automation and evaluation tooling using Python or equivalent languages, replacing manual checks with repeatable engineering controls

Act as a technical SME for AI and cloud security incidents, architecture reviews, and engineering remediation, while translating emerging threats into practical controls for internal and client teams

What you bring

7+ years of hands-on experience in security engineering, application security, cloud security, DevSecOps, or related roles, with practical experience securing AI/ML or Generative AI workloads

Strong DevSecOps experience covering CI/CD security, container and Kubernetes security, infrastructure-as-code, vulnerability management, and software supply-chain security

Hands-on understanding of modern AI architectures including model APIs, RAG, vector/retrieval systems, orchestration frameworks, and AI agents

Strong knowledge of application and API security, IAM/workload identity, secrets management, data protection, and cloud-native security controls

Deep expertise in at least one major cloud platform such as AWS, Azure, or GCP, with the ability to apply equivalent security patterns across cloud environments

Familiarity with AI security and governance frameworks such as OWASP guidance for LLM/Generative AI applications, MITRE ATLAS, and NIST AI RMF

Python or equivalent programming capability for security automation, testing, and evaluation tooling

Ability to independently own complex security problems from threat modelling and architecture through implementation, validation, and remediation