Responsible AI Model Assessor

1 day ago

Thrissur, Kerala, India beBeeEvaluator Full time ₹ 1,00,00,000 - ₹ 2,01,00,000
Job Description

We are seeking a highly skilled and principled AI Evaluator to assess, audit, and ensure the ethical development and deployment of AI models across our enterprise.

This role spans traditional ML models, Large Language Models (LLMs), and Generative AI systems, with a strong focus on fairness, transparency, privacy, security, and accountability across multi-cloud environments.

The ideal candidate combines deep technical expertise with a firm grasp of ethical frameworks, regulatory requirements, and responsible AI principles.

We are looking for someone who can evaluate AI/ML, LLM, and GenAI models for bias, fairness, explainability, and robustness.

Requirements
  • 5+ years of experience in AI/ML evaluation, testing, or assurance roles.
  • Proven experience with LLMs (e.g., GPT, Claude, LLaMA) and generative AI (e.g., DALL·E, Midjourney, custom diffusion models).
  • Strong knowledge of fairness, bias detection, differential privacy, red-teaming, model transparency techniques.
  • Familiarity with AI regulation and governance (EU AI Act, FTC guidance, DSA, HIPAA, etc.).
  • Experience with cloud platforms (AWS, Azure, GCP) and deploying/evaluating AI services in distributed architectures.
  • Proficiency in Python and libraries such as scikit-learn, Transformers, LangChain, MLflow, PyTorch, etc.
  • Strong communication and reporting skills, with the ability to translate technical findings into business risk language.
Benefits

Join us at the cutting edge of AI ethics, safety, and trust.

Collaborate with interdisciplinary teams across engineering, legal, and policy.

Influence how AI is responsibly used across industries and sectors.

Shape a safer and more accountable AI future.

Additional Info

Mandatory qualifications include a Master's or PhD in Computer Science, Data Science, Ethics, or related field.

Prior work with model evaluation benchmarks and large-scale LLM testing frameworks is also an asset.

Familiarity with secure and privacy-preserving ML techniques (e.g., federated learning, homomorphic encryption) would be beneficial.

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