Writer - Financial Services

1 week ago

Andhra Pradesh, India Innodata Inc. Full time
INOD) is a leading data engineering company. With more than 2,000 customers and operations in 13 cities around the world, we are an AI technology solutions provider-of-choice for 4 out of 5 of the world’s biggest technology companies, as well as leading companies across financial services, insurance, technology, law, and medicine. By combining advanced machine learning and artificial intelligence (ML/AI) technologies, a global workforce of subject matter experts, and a high-security infrastructure, we’re helping usher in the promise of AI. Innodata offers a powerful combination of both digital data solutions and easy-to-use, high-quality platforms.

Our global workforce includes over 5,000 employees in the United States, Canada, United Kingdom, the Philippines, India, Sri Lanka, Israel and Germany.

We are seeking highly analytical and detail-oriented professionals with hands-on experience in Red Teaming, Prompt Evaluation , and AI/LLM Quality Assurance . The ideal candidate will help us rigorously test and evaluate AI-generated content to identify vulnerabilities, assess risks, and ensure compliance with safety, ethical, and quality standards.

Job Title: AI Red Teaming & Prompt Evaluation Specialist
Conduct Red Teaming exercises to identify adversarial, harmful, or unsafe outputs from large language models (LLMs).
Evaluate and stress-test AI prompts across multiple domains (e.g., finance, healthcare, security) to uncover potential failure modes.
Collaborate with data scientists, safety researchers, and prompt engineers to report risks and suggest mitigations.
Perform manual QA and content validation across model versions, ensuring factual consistency, coherence, and guideline adherence.
Create evaluation frameworks and scoring rubrics for prompt performance and safety compliance.
Proven experience in AI red teaming , LLM safety testing, or adversarial prompt design.
Familiarity with prompt engineering , NLP tasks, and ethical considerations in generative AI.
Strong background in Quality Assurance , content review, or test case development for AI/ML systems.
Understanding of LLM behaviours, failure modes, and model evaluation metrics.

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