AI Governance Engineer
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Key skills for this role
About the Role
talabat is seeking an AI Governance Engineer to own standard-setting, testing, and independent verification of AI agent safety, quality, and compliance. You will define what safe, correct, and compliant means, test for it at scale, and drive accountability.
Key Skills for This Role
Responsibilities
- Architect adversarial payload libraries and red teaming frameworks that Engineering teams use to self test their agents
- Author the reusable red teaming library covering OWASP Top Vulnerabilities for LLM Applications and specific agent patterns
- Enable teams to verify data leakage prevention and tool guardrails autonomously through standardized verification suites
- Build hallucination testing policies, bias assessment templates, and drift monitoring frameworks as consumable services
- Enable teams to integrate evaluation methodologies directly into CI/CD pipelines
- Translate regulatory obligations into actionable technical checklists and mapping tools for Engineering
- Maintain a bird's eye view of all AI products through automated monitoring and reporting of governance metrics
- Conduct periodic maturity assessments against global frameworks (NIST AI RMF, EU AI Act) and provide roadmaps for compliance gaps
Requirements
- Extensive experience building and testing systems where safety, compliance, or safety matter
- Experience in red teaming or adversarial testing on LLM applications, or AI Engineering, QA automation, compliance engineering or red teaming with credible ability to transfer skills to AI risks
- Understanding of data access control models (IAM, row level security, policy based access control)
- Knowledge of how RAG pipelines and agent tool calling work
- Ability to code (Python preferred) to build test harnesses, automate scans, and parse logs
- Experience working in regulated environments (fintech, healthtech, finserv, government)
- Understanding of prompt injection defence patterns and OWASP Top Vulnerabilities for LLMs
Full Job Posting
Company Description
- talabat is the leading on demand food and non food delivery platform in MENA, operating across 8 countries.
- We're part of Delivery Hero, the global leader in online food delivery and q commerce, and we're engineering first.
The Role AI Governance Engineer
- You own the standard setting, testing, and independent verification of AI agent safety, quality, and compliance.
- You're not building agents or operating them. You define what 'safe, correct, and compliant' means, test for it independently, and drive accountability.
- As agent count climbs, you'll scale through risk tiering and reusable test libraries.
What You'll Own AI Safety
- Architect adversarial payload libraries and red teaming frameworks that Engineering teams use to self test their agents.
- Author the reusable red teaming library covering OWASP Top Vulnerabilities for LLM Applications and your specific agent patterns.
- Enable teams to verify data leakage prevention and tool guardrails autonomously through standardized verification suites.
- Provide periodic red team campaigns and automated regression scripts that empower teams to own their safety posture.
What You'll Own Quality
- Build hallucination testing policies, bias assessment templates, and drift monitoring frameworks as consumable services.
- Enable teams to integrate evaluation methodologies directly into CI/CD pipelines, ensuring quality is built in by design.
- Provide automated triggers that allow teams to spot check production conversations and maintain high quality.
What You'll Own Compliance & Brand Success
- Translate regulatory obligations into actionable technical checklists and mapping tools that Engineering can execute independently.
- Provide frameworks for auditability and brand safety sampling, operationalizing compliance requirements in real time.
- Bridge Legal and Engineering with technical verification tools—governance as an accelerator, not a roadblock.
What You'll Own Governance Intelligence
- Maintain a bird's eye view of all AI products through automated monitoring and reporting of governance metrics.
- Conduct periodic maturity assessments against global frameworks (NIST AI RMF, EU AI Act) and provide roadmaps for compliance gaps.
- Liaise with teams to troubleshoot and reach target scores when metrics fall below thresholds.
Your Profile
- You have extensive experience building and testing systems where safety, compliance, or safety matter.
- You've done red teaming or adversarial testing on LLM applications, or worked in AI Engineering, QA automation, compliance engineering or red teaming.
- You understand data access control models (IAM, row level security, policy based access control).
- You know how RAG pipelines and agent tool calling work.
- You can code (Python preferred) to build test harnesses, automate scans, and parse logs.
What Sets You Apart
- You speak the language of security, engineering, data, and legal—constantly translating between them.
- You're comfortable with technical pushback and can articulate trade offs between governance strictness and agent velocity.
- You've shipped governance standards or compliance processes across teams and worked in regulated environments.
- You understand prompt injection defence patterns, know OWASP Top Vulnerabilities for LLMs, and can independently evaluate governance frameworks.
How You'll Progress
- Initial Phase: Audit all agents, define governance taxonomy, execute first red team campaigns, build cross functional relationships.
- Operationalize: Deploy reusable red teaming libraries, automated regression testing, data leakage standards, hallucination evaluation frameworks.
- Scale & Stratify: Implement risk tiering logic, scale automated red teaming, build self serve governance libraries, deploy drift monitoring dashboards.
- Embedded & Strategic: Fully automated governance with zero silent drift incidents; governance viewed as enabler, not blocker.
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