Enterprise AI Governance & Trust Layer Engineer

Lever, Inc.; Turkey; Remote; TRY 500,000 - 700,000; Full time

Benefits offered by this job

Fully remote

Offshore engagement

Enterprise AI governance work

Generative AI security exposure

Collaborative security teams

Technical ownership

Job summary

Lever, Inc. on behalf of a partner company seeks an Enterprise AI Governance & Trust Layer Engineer based in Turkey for a fully remote role focused on securing and governing enterprise use of generative AI.

You’ll design trust and privacy layers, implement real-time PII detection, policy-as-code guardrails, audit trails, and threat defenses, collaborating with security and data teams to strengthen enterprise AI controls.

Qualifications

• 5–9 years of engineering experience, including at least 3 years 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.
• Understanding of AI security risks: prompt injection, jailbreaks, data leakage, data drift, zero-data-retention API models.
• Experience engineering privacy layers, governance controls, or security trust layers between enterprise systems and LLM-based apps.
• Strong understanding of authentication, authorization, secure API design, data protection, and enterprise access-control principles.
• Ability to translate privacy and security requirements into practical automated guardrails.
• Strong analytical and problem-solving skills for AI security and data-flow issues.
• Excellent collaboration and communication with security, data engineering, and other stakeholders.
• CISSP/CDP or relevant cloud security certification mandatory.
• Salesforce Einstein Trust Layer or comparable platforms is a plus.
• Familiarity with vector embeddings and custom text-classification models is desirable.

Responsibilities

• Design and deploy enterprise AI trust layers and governance middleware to establish secure data boundaries between internal systems, apps, and LLMs.
• Implement real-time PII detection and masking using regex, NER, tokenization, and data-classification tech.
• Develop policy-as-code guardrails for privacy, compliance, and sovereignty per GDPR/CCPA/HIPAA.
• Build immutable AI transaction audit trails covering model inputs/outputs, token usage, access activity, etc.
• Implement toxicity, bias, and content-safety controls using moderation models and gates.
• Defend against prompt injection, jailbreaks, and malicious payloads via secure input parsing and validation.
• Configure secure API proxy architectures, OAuth2, RBAC, and centralized access controls.
• Collaborate with security and data teams to integrate governance into AI workflows and strengthen security posture.

Skills

Python

Regular expressions

API architecture

Cloud security

AI safety governance

Data privacy

RBAC / access control

Threat modeling

Collaboration

Problem solving

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 Turkey.

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.