Enterprise AI Governance & Trust Layer Engineer

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 Belgium.

• 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.