Senior AI Security Engineer - Data & AI Platform

EPAM Systems is a global leader in AI transformation engineering and integrated consulting, serving Forbes Global 2000 companies and ambitious startups. They have over thirty years of expertise in custom software, product, and platform engineering. The role is within the AI Foundation team, focused on securing Enterprise AI and Scientific AI workloads across Azure and GCP platforms.

• 5+ years of experience in SDLC foundations and cloud platform engineering with an AI/ML focus
• Security-first mindset with ability to translate control intent into defensible implementations alongside senior specialists
• Hands-on experience with Azure, GCP, and Kubernetes (AKS/GKE) including GPU node pools and VNet/VPC segmentation
• Expertise in Terraform, GitOps workflows, and policy-as-code within CI/CD pipelines (Azure DevOps, GitHub Actions)
• Knowledge of IAM concepts including RBAC/ABAC least-privilege, managed/workload identity, and geo-aware risk-based access controls
• Familiarity with hardened API integration layers, centralized MCP gateways, and LLM guardrails for prompt/response injection and content filtering
• Proficiency in observability tools such as OpenTelemetry, Prometheus, Loki, Tempo, and Grafana for anomaly and drift detection
• Understanding of data classification, training-data/model isolation, and model-theft prevention practices
• Background in compliance frameworks including ISO 27001, EU AI Act, and GDPR
• English proficiency at B2 level or higher

• 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 proving adherence to control descriptions
• Implement Kubernetes GPU node pool segmentation, VNet/VPC isolation, and private endpoints across Azure and GCP
• Configure RBAC/ABAC least-privilege 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 to ensure implementations pass governance consistently
• Support classification and compliance efforts aligned with ISO 27001, EU AI Act, and GDPR requirements

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