Enterprise AI Governance & Trust Layer Engineer

Lever, Inc.; Österreich; Remote; EUR 90.000 - 130.000; Vollzeit

Benefits dieser Stelle

Fully remote working arrangement

Offshore work model

Exposure to enterprise AI governance,

Zusammenfassung

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

You will design trust layers, implement real-time PII protection, and build policy-as-code guardrails to meet GDPR, CCPA, and HIPAA. Collaborate across security and data teams to strengthen AI controls and auditability.

Qualifikationen

• 5–9 years of engineering experience, including 3+ years in AI safety, privacy, governance, or security pipelines.
• Strong Python, regex, data classification, API design, and cloud security knowledge.
• Experience building privacy layers and governance controls between enterprise systems and LLM apps.
• Understanding of authentication, authorization, secure API design and data protection.
• Ability to translate privacy and security requirements into automated guardrails.
• Excellent collaboration and communication with security and data teams.
• A CISSP, CDP, or equivalent cloud security certification is mandatory.

Aufgaben

• Design and deploy enterprise AI trust layers and governance middleware.
• Implement real-time PII detection, masking, and data-classification tech.
• Develop policy-as-code guardrails for privacy, compliance, and sovereignty.
• Build immutable AI transaction audit trails for governance and forensics.
• Apply toxicity and content-safety controls to prevent harmful outputs.
• Defend against prompt injection and jailbreak attempts with secure input parsing.
• Configure secure API proxies, OAuth 2.0, RBAC and centralized access control.
• Collaborate with security and data teams to integrate governance into workflows.

Kenntnisse

Python

Regular expressions

Data classification

API architecture

Cloud security

AI safety

Privacy layers

Security governance

CISSP/CDP

Salesforce Einstein Trust Layer

Vector embeddings

RBAC / authentication

Collaboration

Ausbildung

CISSP / CDP certification

Tools

OAuth 2.0

RBAC

Encryption standards

Jobbeschreibung

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 Austria.

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.

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