AI Security Engineer

Job Title: AI Security Engineer

Location: Remote

Type: 6 months Contract to Hire

Role overview
You will build and integrate the security guardrails that make AI usable at scale: policyascode, proxy layers for model access, prompt/content filtering, evaluation harnesses, secrets & key management, telemetry, and automation in CI/CD. You ll prototype quickly (PoCs), harden what works, and partner with platform, data, and product teams to get controls into production on Google Cloud with modern DevOps practices.

What you ll do

• Build secure AI access layers(Python) for internal use and servicetoservice scenarios: request/response inspectors, output redaction, rate limiting, and audit logging. Integrate with sensitivity labels/DLP and identity controls where applicable.
• Develop agent safety patterns(for orchestration frameworks) including tooluse allowlists, function sandboxing, constrained retrieval, and memory hygiene; create reusable modules for product teams.
• Implement and operate evaluation pipelines(redteam prompts, jailbreak detection, toxicity/PII checks, hallucination/grounding scores) as part of CI/CD gating releases on eval thresholds; capture artifacts for 5Rs evidence.
• Engineer Google Cloud Platform security controlsfor AI workloads: VPCSC, private service connect, service account hygiene, Workload Identity Federation, CMEK, Secret Manager, Cloud Build/Artifact Registry policies, Cloud Logging/Monitoring/SCC alerting.
• Harden data pipelinesfeeding models (poisoning/tamper detection, provenance/lineage, RBAC/ABAC, DLP), working with data engineering teams.
• Automate controls(policyascode) to enforce least privilege, environment isolation, egress controls, and artifact signing; integrate with existing SAST/DAST/SCA and threatmodeling workflows.
• Contribute to Copilot security enablement: configure Purview sensitivity, Copilot DLP, Restricted Access sites, and Conditional Access for AI apps; validate via test plans.
• Ingest architecture diagrams, dataflow specs and service metadata to produce LLMassisted Security use-cases (leveraging AI for security).
• Engineer autonomoassisted SOC agentsto ingest alerts from Defender XDR/Sentinel and approved thirdparty sources, perform enrichment

What you ll bring

• Strong software engineering in Python(frameworks, testing, packaging) with experience building secure services/middletiers and AI agent
• Handson Google Cloud expertise (IAM, GKE/Cloud Run, Cloud Build, Artifact Registry, Secret Manager, VPCSC, SCC) and DevOps (IaC, CI/CD, policyascode).
• Practical knowledge of AI threats & mitigations (prompt injection filters, content moderation, output redaction, tokenlevel guardrails, secrets hygiene, model endpoint hardening).
• Familiarity with enterprise collaboration controls(Purview labels, DLP for Copilot, restricted access sites) and how to test their efficacy.

Nice to have

• Experience wiring evaluations/redteam harnesses into CI (e.g., blocking merges on eval regressions); exposure to EU AI Act/GDPRimplications for logging/telemetry and DPIAs.
• Knowledge of SAST/DAST/SCA and dependency governance aligned to our SDLC standards.