Senior AI Security Engineer - Data & AI Platform
New
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EPAMAI security
Opportunity to work remotely within PolandFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- English proficiency at B2 level or higher
- Experience
- 5+ years of experience in SDLC foundations and cloud platform engineering with an AI/ML focus
- Required Skills
- GCPKubernetesAzureTerraform
Requirements
- Have 5+ years of experience in SDLC foundations and cloud platform engineering with an AI/ML focus.
- Bring hands-on experience with Azure, GCP, and Kubernetes (AKS/GKE), including GPU node pools and VNet/VPC segmentation.
- Have expertise in Terraform, GitOps workflows, and policy-as-code within CI/CD pipelines, including Azure DevOps or GitHub Actions.
- Understand IAM concepts including RBAC/ABAC least privilege, managed/workload identity, and geo-aware risk-based access controls.
- Be familiar with hardened API integration layers, centralized MCP gateways, and LLM guardrails for prompt/response injection and content filtering.
- Be proficient with observability tools such as OpenTelemetry, Prometheus, Loki, Tempo, and Grafana.
- Understand data classification, training-data/model isolation, and model-theft prevention practices.
- Have a background in compliance frameworks including ISO 27001, EU AI Act, and GDPR.
- Have English proficiency at B2 level or higher.
- Be able to translate control intent into defensible implementations alongside senior specialists.
Responsibilities
- Translate AI Security control descriptions into working platform configurations and defensible implementations.
- Engineer security controls as code using Terraform, GitOps CI/CD pipelines, and policy-as-code frameworks.
- Produce machine-verifiable evidence of adherence to control descriptions.
- Implement Kubernetes GPU node pool segmentation, VNet/VPC isolation, and private endpoints across Azure and GCP.
- Configure least-privilege RBAC/ABAC access, managed/workload identity, and risk-based access controls.
- Build a hardened API integration layer and centralized gateway with tool-level access controls.
- Deploy LLM guardrails for prompt/response injection, content filtering, kill-switches, and inference rate limits.
- Set up observability pipelines for anomaly and drift detection, restricted-access logs, and end-to-end traceability.
- Maintain training-data and model isolation to support robustness and prevent model theft.
- Collaborate with Senior AI Security auditors and AI Engineers and support classification and compliance efforts.
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