Enterprise AI Governance & Trust Layer Engineer

Lever, Inc.; Town of Belgium (WI); Remote; USD 136,000 - 204,000; Full time

Benefits offered by this job

Fully remote working arrangement

Contract engagement with offshore work

Job summary

Lever, Inc. is seeking an Enterprise AI Governance & Trust Layer Engineer to design and deploy secure governance layers for enterprise AI. The role focuses on real-time privacy, policy enforcement, and auditable AI transactions across integrated systems.

The ideal candidate has 5–9 years of engineering experience with 3+ years in AI safety and governance, strong Python and API skills, and a mandatory CISSP/CDP certification. This is a fully remote, offshore-friendly engagement.

Qualifications

• 5-9 years of engineering experience with 3+ years in AI safety, privacy, governance or security pipelines.
• Strong Python, regex, data classification, API design and cloud security knowledge.
• Understanding of AI security risks: prompt injection, data leakage, token constraints, zero-data APIs.
• Experience building privacy layers and governance controls between systems and LLM apps.
• Fluency in authentication, authorization, secure API design and enterprise access controls.
• Ability to translate privacy/security needs into automated guardrails.
• Strong analytical and collaborative skills with security and data teams.
• A CISSP/CDP or equivalent cloud security cert is mandatory.

Responsibilities

• Design and deploy enterprise AI trust layers and governance middleware to secure data boundaries.
• Implement real-time PII detection and masking using regex, NER, and data classification.
• Develop policy-as-code guardrails for privacy, compliance, and sovereignty (GDPR, CCPA, HIPAA).
• Build immutable AI transaction audit trails covering inputs/outputs, token usage, access activity.
• Implement toxicity, bias, and content-safety controls via moderation models and gates.
• Defend against prompt injections, jailbreaks, and malicious payloads with secure input parsing.
• Configure secure API proxies, OAuth2, RBAC, and centralized access controls across AI systems.
• Collaborate with security and data teams to weave governance into enterprise workflows.

Skills

Python

Regex

API architecture

Cloud security

Data privacy

RBAC

Security governance

AI safety

Threat modeling

Education

CISSP/CDP or cloud security certification

Tools

Salesforce Einstein Trust Layer

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 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.