Responsible AI Engineer

In this role, you will:

• Design and implement runtime guardrails and safety architectures for LLM and agentic AI systems, including input/output controls, prompt injection detection, and policy enforcement mechanisms
• Lead red-teaming and adversarial testing initiatives to identify vulnerabilities such as jailbreaks, prompt injections, and trust boundary violations before production deployment
• Build and operationalize AI governance and compliance frameworks, translating regulations (e.g., EU AI Act, NIST AI RMF) into enforceable engineering controls
• Develop and deploy fairness, bias, and model evaluation pipelines, including hallucination detection, groundedness validation, and subgroup performance analysis
• Establish observability and auditability for AI systems through structured logging, audit trails, governance metrics, and incident response processes

What you need to have to be considered

• Experience in software engineering or ML engineering, with hands-on exposure to AI safety, governance, or trust engineering
• Proven experience building and deploying LLM or agentic AI systems in enterprise or regulated environments
• Strong expertise in at least two of the following: guardrails engineering, red teaming, bias/fairness evaluation, AI regulatory compliance
• Working knowledge of AI regulatory frameworks such as EU AI Act, NIST AI RMF, ISO/IEC 42001, or sector-specific compliance standards
• Strong Python programming skills, with experience building evaluation pipelines, observability systems, or AI governance tooling
• Ability to collaborate with security, legal, and risk stakeholders, translating technical AI risks into actionable insights for business leaders
• Excellent communication and executive-level presentation skills, with experience presenting to senior client stakeholders

These will help you stand out

• Hands-on experience conducting red-team assessments on production AI systems and remediating critical vulnerabilities
• Experience creating audit-ready AI governance and compliance documentation for external or regulatory review
• Ability to influence AI system design decisions by balancing technical feasibility with regulatory and risk considerations