About the company
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an Enterprise AI Governance & Trust Layer Engineer based in Israel.
Responsibilities
- Design and deploy enterprise AI trust layers and governance middleware that establish secure data boundaries between internal systems, enterprise applications, and foundational LLMs.
- Implement real-time PII detection and masking using regular expressions, Named Entity Recognition (NER), tokenization, and related data-classification technologies.
- Develop policy-as-code guardrails for data privacy, compliance, and sovereignty requirements, including frameworks aligned with regulations such as GDPR, CCPA, and HIPAA.
- Build automated and immutable AI transaction audit trails covering model inputs and outputs, token usage, access activity, and other information required for monitoring and forensic analysis.
- Implement toxicity, bias, and content-safety controls using moderation models and classification gates to prevent harmful or non-compliant outputs.
- Develop defenses against prompt injection, jailbreaks, malicious payloads, and attempts to override system instructions through secure input parsing and validation mechanisms.
- Configure secure API proxy architectures, OAuth 2.0 authentication and validation flows, RBAC, and centralized access controls across integrated AI systems.
- Collaborate with security, data engineering, and other technical teams to integrate governance controls into enterprise AI workflows and continuously strengthen the overall security posture.
Requirements
- 5–9 years of overall engineering experience, including at least 3 years specifically designing, building, and maintaining AI safety, privacy, governance, or security pipelines.
- Strong proficiency in Python, regular expressions, automated data classification, API architecture, and cloud security frameworks.
- Demonstrated understanding of AI security risks, including prompt injection, jailbreaks, data leakage, data drift, token transmission constraints, and zero-data-retention API models.
- Experience engineering privacy layers, governance controls, or security trust layers between enterprise systems and LLM-based applications.
- Strong understanding of authentication, authorization, secure API design, data protection, and enterprise access-control principles.
- Ability to translate privacy and security requirements into practical technical controls and automated guardrails.
- Strong analytical and problem-solving skills, with the ability to investigate complex AI security and data-flow issues.
- Excellent collaboration and communication skills when working with security, data engineering, and other technical stakeholders.
- CISSP, Certified DevSecOps Professional (CDP), or relevant cloud security specialty certification is mandatory.
- Experience with Salesforce Einstein Trust Layer or comparable enterprise AI safety platforms is an advantage.
- Familiarity with vector embeddings and custom text-classification models for identifying nuanced enterprise intellectual-property or data leaks is desirable.
Conditions
- Fully remote working arrangement.
- Contract engagement with an offshore work model.
- Opportunity to work on enterprise AI governance, privacy, security, and trust infrastructure.
- Exposure to generative AI security challenges involving LLMs, data protection, compliance, and adversarial inputs.
- Opportunity to collaborate with security and data engineering teams on enterprise-scale AI controls.
- Role with significant technical ownership across AI governance middleware, privacy boundaries, monitoring, and security architecture.