Job Summary

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

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.

Skill Requirements

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.

Other Requirements

Experience Level

• 14+ years of overall experience in software, data, or AI engineering.
• 2–4+ 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.