Senior AI Security Engineer - Data & AI Platform

EPAM Systems Inc; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

EPAM Systems Inc. seeks a Senior AI Security Engineer to secure AI workloads across Azure and GCP. Translate controls into platform configurations, implement security as code with Terraform and GitOps, and attest compliance with governance standards.

You will build hardened API layers, guardrails for LLMs, and observability pipelines, collaborating with auditors and engineers to ensure ISO 27001, EU AI Act, and GDPR alignment.

Qualifications

• 5+ years of experience in SDLC foundations and cloud platform engineering with an AI/ML focus.
• Security-first mindset translating control intent into defensible implementations.
• 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 and workload identity.
• Familiarity with hardened API integration layers and centralized MCP gateways.
• LLM guardrails for prompt/response injection and content filtering.
• Observability tools for anomaly and drift detection (OpenTelemetry/Prometheus/Grafana).
• Understanding of data classification, training-data/model isolation, and model-theft prevention.
• Compliance frameworks ISO 27001, EU AI Act, and GDPR.
• English proficiency at B2 level or higher.

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 that proves 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.
• Support classification and compliance efforts aligned with ISO 27001, EU AI Act, and GDPR requirements.

Skills

SDLC foundations

Cloud platform engineering

AI/ML focus

Azure

GCP

Kubernetes

Terraform

GitOps

Policy as code

IAM concepts

Tools

OpenTelemetry

Prometheus

Grafana

Loki

Tempo

Job description

We are seeking a Senior AI Security Engineer to join the AI Foundation team and secure Enterprise AI and Scientific AI workloads across Azure and GCP platforms. This is an implementation-focused role where you will translate AI Security Framework controls into working platform configurations, engineering security as code through Terraform, GitOps CI/CD security gates, and policy-as-code that continuously attests to compliance. You will collaborate closely with Senior AI Security auditors and AI Engineers in a high-talent-density domain, ensuring implementations consistently pass governance requirements.

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 that proves 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

Requirements

• 5+ years of experience in SDLC foundations and cloud platform engineering with an AI/ML focus
• Security-first mindset with the 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