Senior Security Engineer, AI Platforms (DevSecOps)

Job summary

Secures AI platforms and agentic workloads within engineering squads by building DevSecOps delivery pathways and foundational controls for AI workloads on AWS. The work includes production CI/CD pipelines with security and policy gates, infrastructure as code across multi-account AWS environments, threat modelling for LLM and agent applications, and controls for identity, data access, guardrails, logging, and auditability.

Qualifications

• 5+ years in cloud, platform or DevSecOps engineering.
• CI/CD you designed and owned in production, with security scanning (SAST, SCA, secrets, container and IaC) and the decisions on what blocks a merge.
• Terraform or CDK across multiple AWS accounts, with hands-on IAM, Organizations and SCPs, networking, KMS, CloudTrail, Config and Security Hub.
• Threat modelling for LLM and agent applications: prompt injection, tool abuse and data exfiltration through tool calls.
• Least-privilege identity for agents and the tools they call, with authenticated requests, source validation and centralised audit trails.
• Policy as code, including turning written security policies or requirements into AWS automation.
• Mapping automated controls to security and governance requirements, or to a framework such as ISO 27001, Essential Eight, APRA CPS 234 or SOC 2.
• A self-service engineering model in production: engineers raise changes as GitHub pull requests, peers review and approve them, and automation deploys them with no manual ServiceNow step.
• Weekly use of AI coding tools (Claude Code, Kiro, Amazon Q Developer, Cursor or Copilot) on real delivery work, with a clear view of where they get things wrong.

Responsibilities

• Design and build CI/CD pipelines (GitHub Actions, GitLab CI or Harness) with security scanning, policy checks and approval gates built in.
• Write Terraform or AWS CDK for multi-account AWS environments, including landing zone guardrails such as SCPs and permission boundaries.
• Threat model LLM and agent applications with the squads building them (STRIDE, MITRE ATLAS), and turn the findings into controls and tests.
• Turn written security policies into policy as code (OPA/Rego, Checkov, cfn-guard, AWS Config rules, Cedar) and enforce it in the pipeline and at runtime.
• Map each automated control to the requirement it satisfies, so risk and audit teams can trace coverage without asking you.
• Build platform-level controls: centralised logging and audit trails (CloudTrail, CloudTrail Lake, Security Hub), authenticated requests (OIDC federation, short-lived credentials) and source validation (branch protection, signed commits, artefact signing and provenance).
• Govern agent access to tools and data: least-privilege scopes, an approval path for new MCP servers and tools, AgentCore Identity, Gateway and Policy, and Bedrock Guardrails.
• Set data boundaries for AI workloads: PII handling in prompts and logs, VPC endpoints for Bedrock, KMS on invocation logs and classification of RAG sources.
• Add AI release gates to the pipeline: guardrail tests, prompt injection suites and eval regressions that block a release.
• Detect agent misbehaviour at runtime with alerts on policy denials and anomalous tool calls, and own the incident runbook for AI workloads.
• Run a security exceptions process where each exception has an owner, an expiry date and a record in code.
• Use AI coding agents and AI-assisted code review in your own work, and coach engineers to do the same with approval gates on agent output.
• Work with platform, security and architecture teams: run workshops, write handover material and explain trade-offs to engineers and security leads.

Skills

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Work schedule

Industry

Relocation