Enterprise AI Governance & Trust Layer Engineer

Lever, Inc.; Spain (TX); Remote; USD 102,000 - 147,000; Full time

Benefits offered by this job

Fully remote working arrangement.

Contract engagement with an offshore工作

Job summary

Lever, Inc. is seeking an Enterprise AI Governance & Trust Layer Engineer for a remote role focused on securing and governing enterprise use of generative AI.

You’ll design trust and privacy layers, implement real-time PII detection, and build audit trails and guardrails to ensure compliant AI interactions across enterprise systems. Collaborate with security, data engineering, and other teams to strengthen the overall security posture, addressing prompt injections and data leakage risks while

Qualifications

• 5–9 years of engineering experience with at least 3 years in AI safety, privacy, governance, or security pipelines.
• Strong proficiency in Python, regex, data classification, API design, and cloud security.
• Experience with privacy and security controls between enterprise systems and LLM apps.
• Ability to translate privacy and security requirements into automated guardrails.
• CISSP, CDP, or equivalent certification mandatory.
• Familiarity with enterprise AI safety platforms desirable.

Responsibilities

• Design and deploy enterprise AI trust layers and governance middleware.
• Implement real-time PII detection and masking.
• Develop policy-as-code guardrails for GDPR, CCPA, HIPAA.
• Build immutable AI transaction audit trails.
• Implement toxicity, bias, and content-safety controls.
• Defend against prompt injection and jailbreaks.
• Configure secure API proxy architectures and OAuth2.
• Collaborate with security and data teams to integrate governance into workflows.

Skills

Python

Regular expressions

Data classification

API design

Cloud security

Privacy controls

AI safety

Governance

RBAC

Collaboration

Education

Certification: CISSP/CDP

Tools

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 Spain.

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.