About the Role
We are seeking a hands‑on Cloud & AI Security Architect with proven enterprise experience securing and delivering AI systems built on AWS Bedrock and/or Azure AI Foundry. This is a delivery‑focused architecture role. Candidates must have directly worked on production GenAI systems, not just designed or advised on them. You will define and implement security architecture for AI‑enabled cloud platforms, ensuring secure‑by‑design implementation across LLM, RAG, and agent‑based systems in a regulated enterprise environment.
Required Experience (must‑have)
Hands‑on delivery of production AI systems using AWS Bedrock and/or Azure AI Foundry
Direct experience securing LLM‑based applications in enterprise environments
Experience building or securing RAG pipelines, AI APIs, or agentic workflows
Implementation of security controls (not just design or governance)
Experience operating in regulated enterprise environments
Key Responsibilities
Design and implement security for GenAI systems using AWS and Azure AI platforms.
Secure LLM applications, including prompt flows, RAG pipelines, and agent workflows.
Define and enforce model access controls, data boundaries, and interaction security.
Implement security architecture across AWS and Azure environments (IAM, federation, least privilege, identity governance).
Apply network security (zero trust, segmentation, private endpoints) and encryption/key management and secrets handling.
Secure CI/CD and DevSecOps integration.
Threat model AI systems (LLMs, agents, orchestration layers) and identify/mitigate risks such as prompt injection, data leakage, and model abuse.
Define guardrails for safe enterprise AI adoption.
Review HLDs/LLDs for cloud and AI systems, ensuring alignment with enterprise security and regulatory requirements, and translate security requirements into implementable engineering controls.
Required Skills
Cloud Security IAM, SSO, RBAC/ABAC models
Cloud network security (VPC/VNet, segmentation, private connectivity)
KMS/HSM, encryption, and secrets management
SIEM integration and security monitoring
DevSecOps / CI‑CD security controls
AI Security (hands‑on required)
Securing LLM applications in production
RAG architecture security
Agentic AI workflow security
Prompt injection and LLM abuse mitigation
AI data governance and access control
Architecture & Delivery: Proven ability to design and implement HLD/LLD in production environments
Experience producing reusable security architecture patterns
Ability to work directly with engineering teams to implement controls
Strong understanding of balancing delivery speed with security requirements
Success Criteria
AI systems on AWS Bedrock / Azure AI Foundry are secure by design
Security patterns are reusable and adopted by engineering teams
AI features can be delivered quickly without introducing unmanaged risk
Clear alignment between AI innovation and enterprise security requirements
#J-18808-Ljbffr
We are seeking a hands‑on Cloud & AI Security Architect with proven enterprise experience securing and delivering AI systems built on AWS Bedrock and/or Azure AI Foundry. This is a delivery‑focused architecture role. Candidates must have directly worked on production GenAI systems, not just designed or advised on them. You will define and implement security architecture for AI‑enabled cloud platforms, ensuring secure‑by‑design implementation across LLM, RAG, and agent‑based systems in a regulated enterprise environment.
Required Experience (must‑have)
Hands‑on delivery of production AI systems using AWS Bedrock and/or Azure AI Foundry
Direct experience securing LLM‑based applications in enterprise environments
Experience building or securing RAG pipelines, AI APIs, or agentic workflows
Implementation of security controls (not just design or governance)
Experience operating in regulated enterprise environments
Key Responsibilities
Design and implement security for GenAI systems using AWS and Azure AI platforms.
Secure LLM applications, including prompt flows, RAG pipelines, and agent workflows.
Define and enforce model access controls, data boundaries, and interaction security.
Implement security architecture across AWS and Azure environments (IAM, federation, least privilege, identity governance).
Apply network security (zero trust, segmentation, private endpoints) and encryption/key management and secrets handling.
Secure CI/CD and DevSecOps integration.
Threat model AI systems (LLMs, agents, orchestration layers) and identify/mitigate risks such as prompt injection, data leakage, and model abuse.
Define guardrails for safe enterprise AI adoption.
Review HLDs/LLDs for cloud and AI systems, ensuring alignment with enterprise security and regulatory requirements, and translate security requirements into implementable engineering controls.
Required Skills
Cloud Security IAM, SSO, RBAC/ABAC models
Cloud network security (VPC/VNet, segmentation, private connectivity)
KMS/HSM, encryption, and secrets management
SIEM integration and security monitoring
DevSecOps / CI‑CD security controls
AI Security (hands‑on required)
Securing LLM applications in production
RAG architecture security
Agentic AI workflow security
Prompt injection and LLM abuse mitigation
AI data governance and access control
Architecture & Delivery: Proven ability to design and implement HLD/LLD in production environments
Experience producing reusable security architecture patterns
Ability to work directly with engineering teams to implement controls
Strong understanding of balancing delivery speed with security requirements
Success Criteria
AI systems on AWS Bedrock / Azure AI Foundry are secure by design
Security patterns are reusable and adopted by engineering teams
AI features can be delivered quickly without introducing unmanaged risk
Clear alignment between AI innovation and enterprise security requirements
#J-18808-Ljbffr
