Posted today · be early
AI and Cloud Security Engineer
TD SYNNEX
IndiaremotePosted today
Skill Required
AI-Security-EngineerCloud-Security-EngineerSecurity-ArchitectSecurity-Operations-EngineerDetection-EngineerAI-Cybersecurity-EngineerCloud-Cybersecurity-EngineerAI-Application-Security-EngineerSenior-Cloud-Infrastructure-Security-EngineerAI EngineerCloud EngineerSecurity EngineerCloud SecuritysecurityCloudandAIGitHub ActionsCybersecurityAzureEngineeringTerraformServerlessDatabricksKubernetesautomationEmbedded CHardhatTestNGdesignDevOpsGitCI/CDOAuthSIEMGenerative AIRAGFulltime
Key highlights
- Experience Level: Senior / L3 escalation point
- Key Benefit: Flexible remote work
- Notable Requirement: Multi-cloud security engineering (Azure, AWS, GCP)
- Notable Requirement: Proficiency in KQL, PowerShell, and Python
Role overview
TD SYNNEX is seeking a senior technical owner for its AI security runtime control plane and detection fabric to support their runtime-first, default-allow security posture across Azure, AWS, and GCP. The role focuses on building and integrating an inline enforcement pipeline where the AI agent acts as the perimeter, ensuring security for AI applications and infrastructure while maintaining compliance and low-friction innovation. Reporting to AI & Cloud Security leadership, the engineer will collaborate with cross-functional teams and IT/application development leadership to turn strategy into operating controls. This includes managing a closed-loop defensive system where red-team findings automatically improve runtime policies and detection logic.
Responsibilities
- 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.
- Support cloud workload vulnerability management (VMs, containers, registries, serverless) with risk-based prioritization, and act as escalation point for investigations spanning cloud, identity, endpoint, and AI telemetry.
Requirements
- 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.
- 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.
- Significant hands-on engineering responsibility, including in cloud security (Azure/AWS/GCP) and demonstrable experience securing or operating AI/ML or LLM-based systems (or strongly evidenced, rapidly acquired hands-on AI-security capability).
- Proven track record deploying, integrating, and tuning security platforms in complex enterprise environments and serving as an L3 / senior escalation point.
- Bachelor's degree in Computer Science, Cybersecurity, Information Systems, or equivalent practical experience.
- Ability to develop automation in both AI and cloud environments with infrastructure as code (IaC) or policy as code principles.
Nice to have
- Hands-on experience with AI security technologies and open-source AI security tooling.
- Experience running vendor proofs-of-concept.
- Experience operating security tooling in Kubernetes-based environments (AKS/EKS) and integrating open-source components into enterprise stacks.
- Relevant certifications: CISSP, CCSP, SC-100, AZ-500, AWS Certified Security – Specialty, Google Professional Cloud Security Engineer.
- AI red-team or AI-security credentials.
Benefits
- Elective Benefits: Programs tailored to your country to best accommodate your lifestyle.
- Grow Your Career: Formal programs on leadership and professional development, and many more on-demand courses.
- Elevate Your Personal Well-Being: Seminars, events, and a global Life Empowerment Assistance Program for financial, physical, and mental well-being.
- Diversity, Equity & Inclusion: Inclusive education, meaningful peer-to-peer conversations, and equitable growth and development opportunities.
- Global Organization Networking: Onboarding program to network with new co-workers within the first 30 days.
- Community Connection: Internal, peer-led inclusive communities, business resource groups, local volunteering events, and environmental and social initiatives.
- Flexible remote work.
Additional details
- The engineer will report to AI & Cloud Security leadership and have strong working relationships with IT and application development leadership.
- Willing to work nonstandard business hours for projects, business impact issues and incident response.
- Some travel required.
- TD SYNNEX values: Together, We Own It, We Dare to Go, We Grow and Win, and We Do the Right Thing.
- Equal opportunity employer committed to building a diverse team.
- Originally posted on Himalayas
