Enterprise AI Governance & Trust Layer Engineer

Lever, Inc.; Netherlands; Remote; EUR 85,000 - 120,000; Full time

Benefits offered by this job

Fully remote work

Job summary

Lever, Inc. is hiring an Enterprise AI Governance & Trust Layer Engineer in the Netherlands, fully remote. You’ll design trust layers and governance middleware to secure data flows between internal systems and LLMs, implementing real-time PII detection, policy-as-code, auditability, and safety controls.

You’ll collaborate with security and data teams to strengthen enterprise AI security. The role focuses on compliant, auditable AI environments with strong emphasis on privacy, data protection,

Qualifications

• 5–9 years of overall engineering experience, including AI safety, privacy, governance, or security pipelines (3+ years for governance/AI safety).
• Strong proficiency in Python, regular expressions, automated data classification, API architecture, and cloud security frameworks.
• Understanding of AI security risks (prompt injection, data leakage, etc.) and enterprise data protection practices.
• Experience building privacy/gov controls between enterprise systems and LLM-based apps; secure API design.
• CISSP, Certified DevSecOps Professional (CDP), or cloud security specialty certification is mandatory.

Responsibilities

• Design and deploy enterprise AI trust layers and governance middleware to secure data boundaries.
• Implement real-time PII detection, masking, and policy-as-code guardrails for privacy/compliance.
• Build audit trails for AI transactions, including inputs/outputs, token usage, and access activity.
• Apply toxicity and content-safety controls to prevent non-compliant outputs.
• Configure secure API proxies, OAuth 2.0, RBAC, and centralized access controls.

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Enterprise AI Governance & Trust Layer Engineer based in Netherlands.

This is a remote engineering role focused on securing and governing enterprise use of generative AI. You’ll design the trust and privacy layers that control how sensitive corporate data flows to and from large language models. The role combines AI security, data privacy, cloud security, API architecture, and compliance engineering. You’ll build real-time protections for PII, prompt injection, harmful content, unauthorized access, and policy violations. You’ll also establish auditability and monitoring so AI interactions can be traced, reviewed, and governed effectively. Working closely with security and data engineering teams, you’ll help create resilient guardrails for enterprise AI environments. This is a high-impact opportunity for an experienced engineer who wants to shape secure and responsible AI infrastructure.

Accountabilities

• 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.
• A 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.

Benefits

• 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.