A leading professional services technology firm is hiring an experienced AI Governance Engineer for a fully remote, full-time role accessible to candidates in Dubai, UAE and worldwide. This is a high-ownership, independent role dedicated to standard-setting, adversarial testing, and independent verification of AI agent safety, quality, and compliance as the client’s agent fleet scales. You will define what “safe, correct, and compliant” means for LLM-powered AI agents — architect and maintain adversarial payload libraries, OWASP LLM red-teaming frameworks, hallucination testing policies, bias assessment templates, and drift monitoring systems — and integrate all of this into CI/CD pipelines that scale governance automatically as the number of deployed AI agents grows.
About the Role — AI Governance Engineer (Remote Full-Time)
Role Type: Full-Time — Fully Remote — Professional Services Environment
Client: Leading Technology Company — AI Agent Fleet Governance & Compliance
Core Scope: AI agent safety · LLM red teaming · OWASP compliance · Hallucination testing · Bias assessment
Tech Focus: Python · RAG pipelines · CI/CD integration · Drift monitoring · Auditability frameworks
Eligibility: Open to all qualified candidates — hired on demonstrated AI governance and security expertise
Why This AI Governance Engineer Role Is a Rare Career Opportunity in 2026
Pioneer Discipline: AI Governance Engineering is one of the fastest-emerging, most urgently needed, and least-crowded specialisms in the global AI industry — you are early in a defining field
Influence at Scale: Define governance standards that protect an entire fleet of AI agents — your frameworks and testing libraries directly shape how the organization’s AI operates safely
Cross-Domain Influence: Operate at the intersection of security, engineering, data science, and legal — a uniquely positioned role with visibility across the entire technical and regulatory organization
Remote Global Team: Work with a globally distributed professional services team — full remote flexibility with a competitive full-time salary
Position Overview
This AI Governance Engineer role is a senior, high-accountability position that owns the independent safety, quality, and compliance verification function for a growing fleet of AI agents at a leading technology professional services client. You will architect adversarial payload libraries and red-teaming frameworks, author reusable test libraries covering OWASP Top Vulnerabilities for LLM Applications, enable autonomous verification of data leakage prevention and tool guardrails, build hallucination testing policies, bias assessment templates, and drift monitoring frameworks, integrate evaluation methodologies into CI/CD pipelines, translate regulatory obligations into actionable technical checklists, provide auditability frameworks and brand safety sampling, maintain automated governance metrics reporting, and conduct periodic maturity assessments against global AI governance frameworks. This role requires the rare combination of hands-on LLM security testing expertise, Python engineering proficiency, regulatory knowledge, and the communication skills to articulate governance trade-offs across security, engineering, data, and legal domains.
Why AI Governance Engineering Is the Most Important Emerging AI Career of 2026: As enterprise AI agent deployments scale rapidly across professional services, financial services, healthcare, and government, the question of who verifies that these agents are safe, accurate, compliant, and free from harmful bias has become one of the most strategically urgent questions in the entire technology industry. AI Governance Engineers who can architect red-teaming frameworks, build OWASP-compliant LLM test libraries, design hallucination detection systems, and integrate governance into CI/CD pipelines are in critically short supply globally — and the professionals who establish themselves in this discipline in 2026 will be positioned at the very center of enterprise AI deployment for the next decade.
Key Responsibilities
Adversarial Testing & Red-Teaming Framework Architecture
- Architect comprehensive adversarial payload libraries and red-teaming frameworks for autonomous self-testing of AI agents — designing systematic attack scenarios that expose safety, accuracy, and compliance vulnerabilities before they can cause harm in production
- Author reusable red-teaming test libraries covering the full OWASP Top Vulnerabilities for LLM Applications — including prompt injection, insecure output handling, training data poisoning, model denial of service, excessive agency, and sensitive information disclosure
- Provide periodic red-team campaigns and automated regression scripts that continuously verify AI agent behavior remains within defined safety and compliance boundaries — even as the underlying models, tools, and data sources evolve
- Scale red-teaming capability through risk-tiering and reusable test library architecture — ensuring that governance testing effort grows efficiently as the AI agent fleet expands, without requiring proportional increases in manual testing resource
Hallucination Testing, Bias Assessment & Drift Monitoring
- Build comprehensive hallucination testing policies that systematically evaluate AI agent responses for factual accuracy, source grounding, and consistency — establishing quantitative hallucination rate thresholds and automated detection mechanisms that flag degrading accuracy before it impacts users
- Develop bias assessment templates that evaluate AI agent outputs for demographic bias, discriminatory patterns, and fairness violations across diverse user populations and use case domains — ensuring the agent fleet operates equitably and in compliance with applicable non-discrimination standards
- Design and implement drift monitoring frameworks that continuously track AI agent performance, behavior patterns, and output distributions over time — detecting meaningful deviations from baseline that indicate model drift, data drift, or emerging safety risks
- Enable autonomous verification of data leakage prevention mechanisms and tool guardrails — ensuring that AI agents cannot access, exfiltrate, or expose sensitive data beyond the boundaries defined by the data access control architecture
CI/CD Integration & Automated Governance Pipelines
- Integrate all evaluation methodologies — red-teaming tests, hallucination checks, bias assessments, and drift monitors — into CI/CD pipelines that run automatically on every model update, prompt change, or tool modification before changes are promoted to production
- Maintain automated monitoring and reporting of governance metrics — building dashboards and alerting systems that give engineering, compliance, and leadership teams real-time visibility into the safety and compliance status of the entire AI agent fleet
- Build Python-based automation infrastructure that enables scalable, programmatic governance testing — writing clean, well-documented, version-controlled code that integrates reliably with the client’s existing engineering and DevOps toolchain
- Design and maintain data access control verification — testing RAG pipeline data access boundaries, tool permission boundaries, and context window data handling to ensure no unauthorized data flows occur within AI agent operations
Regulatory Translation, Auditability & Compliance Frameworks
- Translate complex regulatory obligations — including AI Act requirements, GDPR implications for AI systems, sector-specific compliance standards, and internal governance policies — into precise, actionable technical checklists that engineering teams can implement and compliance teams can audit
- Provide comprehensive frameworks for auditability and brand safety sampling — ensuring that all AI agent interactions can be retrospectively reviewed, that governance evidence is systematically preserved, and that brand safety violations are detected and flagged in near-real time
- Conduct periodic maturity assessments of the AI governance function against global frameworks — measuring progress against established AI governance standards, identifying gaps, and developing improvement roadmaps that systematically advance the organization’s governance maturity
- Drive accountability when AI agent safety, quality, or compliance issues are identified — articulating root causes clearly, specifying required remediation actions, and following through to verify that fixes are properly implemented and validated before resuming production operation
Qualifications
Core Experience
- Extensive professional experience in building and testing systems where safety, compliance, or security are primary concerns — whether in AI, cybersecurity, regulated financial services, healthcare technology, or critical infrastructure engineering contexts
- Hands-on experience in red-teaming or adversarial testing specifically on LLM applications — with demonstrated ability to identify, exploit, and document AI-specific vulnerabilities using structured attack methodologies
- Knowledge of data access control models and RAG pipeline architectures — including an understanding of how data flows through retrieval-augmented generation systems and where security and data leakage risks arise
- Proficiency in Python — able to write production-quality automation code for governance testing pipelines, CI/CD integration scripts, and monitoring infrastructure independently
The Ideal Candidate Profile
- A skilled cross-domain communicator who can translate fluently between security engineering, AI research, data science, and legal/compliance domains — making governance requirements understandable and actionable across all four communities simultaneously
- Comfortable with technical pushback — able to articulate governance trade-offs between safety strictness and AI agent deployment velocity with evidence-based reasoning that earns respect from both engineering and compliance stakeholders
- Experience in regulated environments — familiarity with how compliance obligations are operationalized in practice, and how governance evidence is collected, maintained, and presented for audit review
- Ability to independently evaluate governance frameworks, make confident decisions under ambiguity, and own the outcomes of those decisions — bringing the personal accountability and professional judgment that a solo-owned governance function demands
About AI Governance Engineering in Dubai 2026
AI Governance Engineering has emerged in 2026 as one of the most critically important and fastest-growing engineering specializations in the entire global technology industry. As organizations across every sector deploy increasingly autonomous AI agents — powered by large language models, retrieval-augmented generation systems, and tool-calling architectures — the question of how to verify, test, and continuously assure the safety, accuracy, fairness, and regulatory compliance of these agents has become urgent at enterprise scale. Traditional software testing methodologies were simply not designed for the probabilistic, context-sensitive, and emergent failure modes that characterize LLM-powered AI systems — and organizations that are deploying AI agents without rigorous, systematized governance frameworks are accumulating safety, reputational, and regulatory risk at pace. For AI engineers with the rare combination of LLM security testing expertise, Python engineering proficiency, and cross-domain communication skills needed to build and operate enterprise-grade AI governance infrastructure, Dubai and the UAE’s rapidly expanding AI adoption landscape — combined with the financial advantages of zero personal income tax — make this one of the most strategically compelling AI career markets in the world in 2026.
Your Career Growth Path: AI Governance Engineer → Senior AI Governance Engineer → Head of AI Safety & Compliance → Chief AI Governance Officer → VP of Responsible AI — a pioneering, globally consequential, and professionally prestigious career trajectory at the absolute frontier of how the world ensures that artificial intelligence operates safely and responsibly at scale.
Who Should Apply?
- AI Security Engineers: With LLM red-teaming, adversarial testing, or prompt injection experience who want to apply these skills in a dedicated AI governance and compliance engineering role
- ML Safety Researchers: With hallucination detection, bias assessment, or AI alignment evaluation experience who want to transition from research into a high-impact professional services engineering context
- DevSecOps Engineers: With CI/CD pipeline experience and security testing automation skills who want to apply their engineering discipline to the emerging field of AI agent governance
- AI Compliance Professionals: With regulatory translation, auditability framework design, and AI governance maturity assessment experience who want a technically hands-on engineering governance role
- RAG & LLM Engineers: With deep knowledge of LLM application architecture, RAG pipelines, and data access control who want to pivot into the governance and safety verification side of AI systems
- UAE-Based AI Professionals: Seeking a fully remote full-time engagement in the rapidly emerging AI governance discipline, contributing to global professional services technology leadership from Dubai
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