Job Summary

Responsible AI (RAI) Engineer

Role Summary

The Responsible AI (RAI) Engineer ensures that AI and Generative AI solutions are ethical, safe, compliant, transparent, and trustworthy throughout their lifecycle. This role focuses on designing, implementing, and enforcing responsible AI practices across AI systems, including LLM-based applications, by embedding fairness, explainability, privacy, security, and governance controls into production environments.

Key Responsibilities

• Embed Responsible AI principles such as fairness, accountability, transparency, safety, and privacy into AI and GenAI solution designs.
• Implement technical controls for bias detection, bias mitigation, explainability, and model governance.
• Design and enforce guardrails for LLM systems including input validation, output moderation, and policy enforcement.
• Conduct AI risk assessments prior to production deployment.
• Ensure compliance with enterprise AI policies, regulatory requirements, and industry standards.
• Implement monitoring mechanisms for hallucinations, harmful outputs, data leakage, and misuse.
• Continuously monitor AI systems for bias, drift, fairness, and ethical risk post-deployment.
• Support audits, architecture reviews, and governance boards.
• Advise engineering and product teams on responsible AI best practices.

Required Skills & Experience

• Strong understanding of AI, ML, and Generative AI systems including LLMs.
• Experience with responsible AI concepts such as fairness, explainability, transparency, and accountability.
• Familiarity with AI governance, risk, and compliance frameworks.
• Programming experience in Python is preferred.
• Experience working with AI monitoring, logging, and audit mechanisms.
• Ability to collaborate with legal, security, compliance, and engineering teams.

Experience Level

• 8+ years of overall experience in software, data, or AI engineering.
• 2+ years of experience in AI risk, governance, security, ethics, or compliance-focused roles.
• Hands-on exposure to GenAI or LLM systems in production environments is highly desirable.

Success Measures

• AI systems deployed with embedded responsible AI controls and governance.
• Reduced ethical, regulatory, and reputational risk from AI deployments.
• Increased enterprise and client trust in AI-driven solutions.

Key Responsibilities

1. To be responsible for providing technical guidance to a team of developers, enhancing their technical capabilities and increasing productivity.
2. To conduct comprehensive code reviews, establish and oversee quality assurance processes, performance optimization , implementation of best practices and coding standards to ensure successful delivery of complex projects.
3. To ensure process compliance in the assigned module| and participate in technical discussions/review as a technical consultant for feasibility study (technical alternatives, best packages, supporting architecture best practices, technical risks, breakdown into components, estimations).
4. To collaborate with stakeholders to define project scope, objectives, deliverables and accordingly prepare and submit status reports for minimizing exposure & closure of escalations.

Skill Requirements

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Other Requirements

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