AI Safety & Evaluation Lead

Responsibilities:

• Design and own the AI evaluation framework: benchmark construction, automated and human-in-the-loop eval pipelines.
• Lead red-teaming and adversarial testing across deployed models: prompt injection, jailbreaking, edge case identification.
• Define AI safety standards, responsible AI policies, and child-safe content guardrails for deployment at scale.
• Build and maintain evaluation tooling: RAGAS, LangSmith, PromptBench, EleutherAI lm- evaluation-harness.
• Conduct systematic bias detection and fairness measurement across outputs, especially for regional and linguistic variation.
• Manage AI risk assessments and translate safety findings into actionable model or prompt changes.
• Establish AI governance documentation: model cards, audit trails, safety reports for internal and regulatory stakeholders.
• Embed evaluation and safety checkpoints into the model release pipeline in collaboration with engineering.
• Track and apply evolving regulatory requirements: EU AI Act, NIST AI RMF, India AI policy frameworks.
• Measure and report model accuracy, grounding fidelity, and response quality across deployed AI systems through systematic evaluation.
• Build and maintain knowledge of AI governance and compliance standards operationalising responsible AI practices into daily engineering workflows.

Requirements:

• Strong experience in AI/LLM safety, evaluation, and risk assessment.
• Expertise in designing and executing model evaluation frameworks and test methodologies.
• Experience in red-teaming, adversarial testing, and identifying model vulnerabilities.
• Strong understanding of bias, fairness, hallucination, and content safety in AI systems.
• Experience measuring model accuracy, grounding, and response quality.
• Knowledge of AI governance, compliance, and responsible AI practices.
• Familiarity with frameworks such as NIST AI RMF and AI regulatory standards; understanding of student psychology and age-appropriate AI output design.
• Strong analytical and problem-solving skills with experience working on production AI systems.

Preferred Skills:

• AI regulations: EU AI Act, NIST AI RMF, DPDP Act (India) and their operational implications.
• Understanding of fine-tuning processes and how alignment tax and behaviour drift occur post-training.
• Explainable AI: SHAP, LIME, attention visualisation, interpretability for non-technical audiences.
• Child safety standards and age-appropriate AI design principles for deployment to minors.
• Cybersecurity fundamentals: threat modelling, adversarial ML, model extraction attack patterns.