Enterprise AI Governance & Trust Layer Engineer
FyerX
- Location
- Remote
- Employment
- Full-time
- Level
- Senior Level
Posted 2 days ago
About the Role
FyerX is seeking an Enterprise AI Governance & Trust Layer Engineer to design and enforce data privacy boundaries across generative AI applications. The role involves building middleware that secures data exchange between enterprise systems and LLMs through real-time PII masking, compliance guardrails, and anti-prompt-injection defenses.
Skills
AI Governance
Data Privacy
PII Masking
Python
Regular Expressions
Named Entity Recognition
Data Classification
API Architecture
Cloud Security
Prompt Injection Defense
Policy-as-Code
OAuth 2.0
Role-Based Access Control
Compliance Monitoring
Toxicity and Bias Filtering
Audit Logging
Perks
- Remote Work
Full job details
This is a remote position.
Enterprise AI Governance & Trust Layer Engineer
Job Details
- Employment Type: Contract
- Work Mode: Remote
- Location: Offshore
- Total Experience Required: 5 to 9 years
- Relevant Experience Required: 3+ years of experience engineering data privacy layers, AI governance controls, and security trust layers between enterprise networks and LLMs
- Mandatory Certification: Certified Information Systems Security Professional (CISSP), Certified DevSecOps Professional (CDP), or AWS Certified Security - Specialty
Job Summary
We are seeking an experienced Enterprise AI Governance & Trust Layer Engineer to design, configure, and enforce data privacy boundaries across our generative AI applications. The ideal candidate will build middleware that intercepts, sanitizes, and audits data passing between internal corporate systems and foundational Large Language Models (LLMs)—implementing real-time PII masking, token validation gates, and compliance monitoring frameworks to secure enterprise AI operations.
Key Responsibilities
- Design and deploy enterprise AI Trust Layers and governance middleware, orchestrating data masking boundaries between internal relational databases and public foundational models.
- Configure real-time Personally Identifiable Information (PII) scrubbers, utilizing advanced regex, Named Entity Recognition (NER), and tokenization engines to mask sensitive fields before LLM submission.
- Implement robust data compliance and sovereignty controls, building policy-as-code guardrails to align model access scripts with strict regional privacy mandates (e.g., GDPR, CCPA, HIPAA).
- Build automated AI transaction audit logs, setting up immutable data logging targets, token usage trackers, and system monitoring loops to track model input-output histories for forensic validation.
- Integrate deep-level toxicity and bias filtering panels, deploying custom moderation models and classification gates to block harmful, inappropriate, or non-compliant model generations.
- Establish anti-prompt-injection defensive rings, writing input parsing scripts to isolate, analyze, and intercept adversarial jailbreaks, system instruction overriding attempts, and malicious payloads.
- Collaborate with security and data engineering squads, configuring custom API proxy setups, secure OAuth 2.0 validation pathways, and centralized data access controls (RBAC) across integrated LLM networks.
Requirements
- engineering experience, with at least 3+ dedicated years actively designing, building, and maintaining AI safety pipelines.
- Strong technical mastery of Python, regular expressions, automated data classification frameworks, API architecture design, and cloud security frameworks.
- Deep structural understanding of prompt injection vulnerabilities, data drift behaviors, token transmission limits, and zero-data-retention API policies.
- Mandatory certification: CISSP, CDP, or a dedicated Cloud Security Specialty credential.
Preferred Qualifications
- Prior experience implementing Salesforce Einstein Trust Layer controls or similar pre-built enterprise platform AI safety gates.
- Familiarity with vector embeddings or custom text classification model tuning to detect nuanced enterprise intellectual property leaks.