Enterprise AI Governance & Trust Layer Engineer

Lever, Inc.; México; A distancia; MXN 1.200.000 - 1.800.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Fully remote

Offshore engagement

Work on enterprise AI governance and安全

Descripción de la vacante

Lever, Inc. is seeking an Enterprise AI Governance & Trust Layer Engineer to work from anywhere with a focus on secure, governed enterprise AI use.

You will design trust layers, implement real-time privacy protections, and build auditable AI interactions across enterprise systems. You will collaborate with security and data teams to deploy governance controls, ensure compliance with privacy regulations, and fortify the AI stack against prompts, leakage, and misuse while maintaining robust access

Formación

• 5–9 years of engineering experience, with at least 3 in AI safety, privacy, governance, or security pipelines.
• Strong proficiency in Python, regex, data classification, API design, and cloud security.
• Understanding AI security risks including prompt injection, data leakage, and zero-data APIs.
• Experience engineering privacy layers and governance between enterprise systems and LLM-based applications.
• Knowledge of authentication, authorization, and enterprise access controls.
• Ability to translate privacy and security requirements into automated guardrails.
• Excellent collaboration and communication with security and data teams.
• Must hold CISSP, CDP, or equivalent cloud security cert.
• Salesforce Einstein Trust Layer experience is a plus.
• Familiarity with vector embeddings and text-classification models is desirable.

Responsabilidades

• 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 regex, NER, tokenization, and related data-classification tech.
• Develop policy-as-code guardrails for data privacy, compliance, and sovereignty requirements aligned with GDPR/CCPA/HIPAA.
• Build automated AI transaction audit trails covering model inputs/outputs, token usage, access activity, for monitoring and forensics.
• Implement toxicity, bias, and content-safety controls using moderation models and gates.
• Develop defenses against prompt injection, jailbreaks, and malicious payloads via secure input parsing and validation.
• Configure secure API proxy architectures, OAuth 2.0 flows, RBAC, and centralized access controls.
• Collaborate with security, data engineering, and other teams to integrate governance controls into enterprise AI workflows.

Conocimientos

Python

Regular expressions

API architecture

Cloud security

AI safety

Privacy governance

Security pipelines

Authentication

Authorization

Access controls

Automation guardrails

Collaboration

Problem solving

Educación

CISSP/CDP or cloud security cert

Herramientas

Salesforce Einstein Trust Layer

Vector embeddings

Descripción del empleo

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 Mexico.

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.