Design and maintain middleware that protects enterprise data exchanged with LLMs through real-time PII masking, access controls, compliance guardrails, and defenses against prompt injection. Build audit and monitoring systems, apply toxicity and bias filters, and collaborate with security and data engineering teams on secure integrations.
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.
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