Senior Manager of AI Safety and Validation Engineering

Client of Discovered MENA

As a Senior Manager of AI Safety and Validation Engineering, you will lead the creation of evaluation frameworks for both predictive Machine Learning and Generative AI.

You will be responsible for defining standardised monitoring protocols and automated guardrails.

Working as an individual contributor, you will provide hands on execution support to various product teams to ensure all deployments meet high performance and compliance standards.

Discover the Responsibilities

• Define standardised evaluation rubrics and scoring systems for system performance and safety across various AI archetypes including RAG and agentic workflows.
• Design comprehensive test cases covering deterministic logic and adversarial scenarios to stress test AI outputs.
• Implement automated evaluators such as LLM-as-a-judge and targeted small language models Deploy explainability techniques like Shapley Values and Integrated Gradients to ensure model transparency and unfair bias detection in lending and fraud models.
• Partner with platform teams to build telemetry pipelines that capture embeddings and inputs while maintaining strict data privacy standards.
• Establish golden signals such as hallucination rates and semantic similarity to trigger automated circuit breakers when performance thresholds are breached.
• Analyse guardrail violations to identify emerging attack patterns and integrate human-in- the-loop corrections into model refinement cycles.
• Translate technical telemetry into actionable model health reports for second-line risk functions Work with specialist software vendors to bring advanced safety capabilities into the internal AI ecosystem

Discover the Requirements

• 7+ years of experience in Machine Learning Engineering, Data Science, or AI Safety and Testing.
• Bachelor s Degree in Computer Science, Information Technology, or a related engineering discipline.
• Expert proficiency in Python and deep knowledge of statistical testing and XAI libraries.
• Hands-on experience with LLM evaluation frameworks such as Ragas, Giskard, or Arize.
• Proven ability to design observability and monitoring systems at scale.
• Prior experience in the financial sector is highly preferred.
• Strong leadership skills with the ability to act as a technical mentor across cross-functional initiatives.