Associate Director, Software Engineering (Guardrail Platform AI Safety Track)

We are currently seeking an experienced professional to join our team in the role of Associate Director, Software Engineering (Guardrail Platform AI Safety Track).

Principal responsibilities:

• Design, develop and productionise AI safety detectors for enterprise AI applications, including prompt injection and jailbreak detection, personally identifiable information and sensitive data detection, data leakage prevention, content safety and harmful content detection, policy violation checks, system prompt leakage detection, and agent or tool-use risk controls.
• Build detector pipelines using multiple approaches, including rule-based detection, pattern matching, NLP / ML classifiers, embedding base models, LLM-as-a-Judge evaluators, and hybrid detection strategies with clear interfaces, confidence scoring and policy-driven actions.
• Develop and optimize detector models and evaluation workflows for guardrails, including test sets creation, data labeling strategies, fine-tuning, model validation, threshold calibration, precision / recall optimization, regression testing, and continuous improvement based on production feedback.
• Evaluate and integrate industry and open-source AI safety solutions, including detector frameworks, safety models and third-party services; perform build-vs-buy analysis, benchmarking, integration, and production readiness assessment.
• Design end-to-end AI safety protection patterns across enterprise AI workloads, including RAG system, coding assistants, conversational AI and agent application covering input, retrieval, generation, output, logging and tool execution layers.
• Build AI Safety observability and governance capabilities, including detector telemetry, tracing, dashboards, evaluation metrics, audit trails, feedback loops and operational controls to monitor safety effectiveness.
• Partner with AI platform, infrastructure, security and application teams to productionise safety solution, including performance optimization, scalability improvement, detector lifecycle management and reliable rollout strategies.

Requirements:

• Experience developing first-party detectors for AI safety detectors, including prompt injection, jailbreak, personally identifiable information, data leakage, toxicity, policy violation or agent safety detection.
• Experience building fine-tuning pipelines for safety-oriented large language models or small language models, including dataset curation, labelling strategy, training workflow implementation, evaluation design, and production validation.
• Experience identifying, evaluating, integrating and productionising third-party or open-source detector solutions, with clear judgement on quality, latency, cost, operational fit and governance requirements.
• Experience making sound build-versus-buy decisions for detector capabilities, and taking either internally developed or externally sourced solutions through evaluation, integration, rollout and ongoing operation in production.
• Experience with AI evaluation or observability tools such as Langfuse, LangSmith, Arize, Phoenix or similar platforms.
• Familiarity with data loss prevention, secret scanning, document classification or enterprise information protection tooling.
• Experience with adversarial testing, red teaming or continuous attack simulation for AI systems.