Enterprise AI Governance & Trust Layer Engineer

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.