AI and Cloud Security Engineer

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Skills you'll use

What you'll do

• Integration — build and own the AI runtime control plane. Engineer, deploy and operate the inline enforcement pipeline for AI traffic: an AI gateway chained with runtime data-protection and intent-based enforcement platforms, fronting LLMs, tools, and MCP servers across Azure AI Foundry, AWS Bedrock, GCP Vertex, Copilot Studio, Databricks, and self-hosted environments.
• Harden the gateway layer for resilience: configure buffering and overload management, enforcement timeouts on external-processing filters, layered global/local rate limiting, and prompt-truncation filters to defend against token-exhaustion and "reasoning overload" denial-of-service attacks.
• Integrate the wider AI security ecosystem into one coherent system: onboard AI asset-intelligence context (agent inventory, ownership, blast radius) into enforcement decisions; connect model-security and supply-chain telemetry and continuous red-team signals into SIEM correlation and SOC workflows; stream unified AI detections, decision logs, and policy events for forensics and audit.
• Policy tuning — enforce without friction. Author and calibrate runtime policies: prompt-injection and jailbreak prevention, PII detection/redaction and data masking, intent-based filtering, tool-call validation, MCP access control, and agent permission boundaries (including "lethal trifecta" prevention for low-code agents). Balance detection versus hard-block modes to minimize false positives, and tune per AI archetype — embedded SaaS copilots, democratized low-code agents, homegrown agentic pipelines, device-based coding agents, and homegrown models.
• Operate the closed loop: translate continuous adversarial-testing findings into runtime policy updates, new detection content, and guardrail tuning, then validate remediation through re-testing — so defenses evolve with attackers rather than remaining static.
• L3 coverage — final escalation for the AI Security toolset. Lead deep investigations of AI-specific attacks (direct/indirect prompt injection, RAG poisoning, memory poisoning, agent hijacking, MCP/tool abuse, model manipulation, AI-driven data exfiltration), perform decision-replay forensics and root-cause analysis, drive automated containment (agent quarantine, permission revocation, kill switch) toward our sub-15-minute containment objectives, and mentor L2 analysts. Troubleshoot any incidents related to policies.
• Evaluate and pilot AI security vendors: run structured proofs-of-concept for runtime defense, AIDR, AI-SPM, AI red-teaming, and model-security platforms, and provide technical recommendations as the stack matures.
• Engineer and operate cloud security controls across Azure, AWS, and GCP — posture policies, detection tuning, and remediation of misconfigurations, excessive entitlements, and workload risks, including the Kubernetes estates (AKS/EKS) hosting the AI gateway layer.
• Design and maintain secure multi-cloud patterns consistent with company approved architectural blueprint as well as zero-trust agent access: least-privilege identity for humans, service principals, and agents (Entra ID, AWS IAM, GCP IAM, PAM), conditional access for AI platforms, and least-privilege OAuth scopes for agent integrations.
• Embed security into DevSecOps: Infrastructure-as-Code (Terraform) scanning, container/Kubernetes security, secrets management, CI/CD hardening, and pre-deployment AI security testing gates in Azure DevOps/GitHub Actions.

Who can apply

• Proven ability to integrate enterprise security platforms into one system: API/connector engineering, log ingestion and normalization, SIEM/SOAR integration, and multi-vendor inline architectures.
• Strong AI/GenAI security depth: LLM and agentic-AI threat models (OWASP LLM Top 10 2025, OWASP Agentic Top 10, MITRE ATLAS), prompt-injection/jailbreak defense, RAG security, AI guardrails and gateways, MCP security, and the AI-SPM/AIDR/AI-red-teaming tooling categories. Hands-on experience with AI security technologies is highly valued; so is experience with open-source AI security tooling.
• Multi-cloud security engineering across Azure, AWS, and GCP: CSPM/CWPP/CNAPP operations, cloud identity and entitlements, Kubernetes and container security, and secure architecture (hub-and-spoke, segmentation, baseline protection).
• Proficiency in KQL, PowerShell, and Python for automation, detection engineering, and tooling; DevSecOps and IaC security (Terraform); CI/CD pipeline integration.
• A pragmatic, enablement-first security mindset: comfortable with a default-allow culture where controls must be fast, precise, and low-friction rather than restrictive.
• Excellent English communication — clear runbooks, integration documentation, and the ability to explain trade-offs to architects, engineers, and leadership across a globally distributed team and multiple time zones.
• Excellent communication and collaboration skills
• Ability to work effectively in a fast-paced, dynamic environment.

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