AI Security Engineer
Posted 15 days ago · 0 applicants
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The role in plain words
This role focuses on securing AI, machine learning, and data-intensive systems. The day-to-day work involves assessing agentic architectures, implementing identity and access controls for AI workloads, and translating security controls into developer-friendly libraries and CI/CD checks.
- 5+ years in Security Engineering/AppSec/Cloud Security (or similar), including 1–2+ years securing AI/ML or data‑intensive systems (GenAI preferred)
- Hands‑on experience with AWS and/or Azure
- Experience with modern app stacks (Python/TypeScript, REST/gRPC, containers/Kubernetes, IaC such as Terraform)
- Practical understanding of LLM attack surfaces (prompt injection, context and goal poisoning, data leakage via tools, training/fine‑tune poisoning, model supply chain) and mitigation patterns
- Experience assessing agentic architectures (LangGraph, CrewAI, or similar) and AI coding assistants (Claude Code, GitHub Copilot) for secure enterprise deployment
- Exposure to Duende IdentityServer, SSO/SCIM, and enterprise authorization patterns
- Familiarity with guardrail tooling (e.g., Azure AI Safety features, Amazon Bedrock Guardrails) and policy engines (OPA/Rego)
- Prior work in AI red‑teaming or safety evaluation harnesses; contributions to OSS or published talks
Extracted from the job description · kept up to date automatically
Who this suits
This role suits security professionals with over five years of experience in security engineering, application security, or cloud security, including specific experience securing AI/ML systems. It is less ideal for those without hands-on experience in AWS/Azure, modern application stacks, or LLM attack surfaces.
Full job description
Original listing · kept for referenceWe are At Cross River, we're building the financial infrastructure that powers global innovation. With our cutting-edge suite of embedded payments, cards, and lending solutions, we enable millions of businesses and consumers to transact seamlessly and securely. With 900+ employees worldwide and an R&D center of over 160 employees in Jerusalem - we’re reshaping how financial technology is developed and delivered. .
Requirements: What You Bring to the Table • 5+ years in Security Engineering/AppSec/Cloud Security (or similar), including 1–2+ years securing AI/ML or data‑intensive systems (GenAI preferred). • Hands‑on experience with AWS and/or Azure and modern app stacks (Python/TypeScript, REST/gRPC, containers/Kubernetes, IaC such as Terraform). • Practical understanding of LLM attack surfaces (prompt injection, context and goal poisoning, data leakage via tools, training/fine‑tune poisoning, model supply chain) and mitigation patterns. • Experience assessing agentic architectures (LangGraph, CrewAI, or similar) and AI coding assistants (Claude Code, GitHub Copilot) for secure enterprise deployment. • Familiarity with identity and access for AI workloads (OAuth2/OIDC, service principals, role tokens, PIM), and secure secret management/KMS. • Experience implementing observability/telemetry and routing findings to SIEM; comfort balancing privacy with traceability. • Ability to translate controls into developer-friendly libraries, docs, and CI/CD checks. • Comfort working in a regulated environment and mapping controls to frameworks (FFIEC, SOC 2, PCI DSS). • Strong written communication in English and Hebrew. Nice to have • Financial services background or other high‑assurance domains. • Exposure to Duende IdentityServer, SSO/SCIM, and enterprise authorization patterns. • Familiarity with guardrail tooling (e.g., Azure AI Safety features, Amazon Bedrock Guardrails) and policy engines (OPA/Rego). • Prior work in AI red‑teaming or safety evaluation harnesses; contributions to OSS or published talks.
Questions about this role
- This listing did not state a salary. We only show pay when the employer publishes it.
- Hybrid
- 5+ years in Security Engineering/AppSec/Cloud Security (or similar), including 1–2+ years securing AI/ML or data‑intensive systems (GenAI preferred), Hands‑on experience with AWS and/or Azure, Experience with modern app stacks (Python/TypeScript, REST/gRPC, containers/Kubernetes, IaC such as Terraform), Practical understanding of LLM attack surfaces (prompt injection, context and goal poisoning, data leakage via tools, training/fine‑tune poisoning, model supply chain) and mitigation patterns, Experience assessing agentic architectures (LangGraph, CrewAI, or similar) and AI coding assistants (Claude Code, GitHub Copilot) for secure enterprise deployment