TS
AI and Cloud Security Engineer
Contract type
Ongoing
Work mode
Remote
Experience
3 years
Job description
Senior technical owner of the AI security runtime control plane and detection fabric at TD SYNNEX, building and operating inline AI enforcement pipelines and serving as L3 escalation for AI and cloud security incidents across a multi-cloud estate.
At a glance
- Work mode
- Remote
- Languages
- English – fluent
What you'll do
- 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
- 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
- Translate continuous adversarial-testing findings into runtime policy updates, new detection content, and guardrail tuning, then validate remediation through re-testing
- 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 sub-15-minute containment objectives, and mentor L2
- Troubleshoot any incidents related to policies
- 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
What we're looking for
- 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
- Willing to work nonstandard business hours for projects, business impact issues and incident response
- Some travel required
Nice to have
- Hands-on experience with AI security technologies is highly valued
- Experience with open-source AI security tooling
- Experience running vendor proofs-of-concept is a plus
- Experience operating security tooling in Kubernetes-based environments (AKS/EKS) and integrating open-source components into enterprise stacks is an advantage
- Relevant certifications strengthen an application (none individually required): CISSP, CCSP, SC-100, AZ-500, AWS Certified Security – Specialty, Google Professional Cloud Security Engineer; AI red-team or AI-security credentials are a plus
Your team
AI & Cloud Security team
A team of two AI & Cloud Security Architects; the role is the engineering force that turns strategy into operating controls across a multi-cloud estate (Azure, AWS, GCP).
Apply
About TD SYNNEX
The world's largest IT distributor
TD SYNNEX (NYSE: SNX) is the world's largest IT distributor, accelerating AI adoption across Azure AI Foundry, AWS Bedrock, Copilot Studio, Databricks, and self-hosted agent environments.
Key facts
- NYSE: SNX
- Tech Data, a TD SYNNEX Company
What they value
- Together
- We Own It
- We Dare to Go
- We Grow and Win
- We Do the Right Thing