Language Model Safety Engineer

5 days ago

Palakkad, Kerala, India beBeeData Full time ₹ 10,00,000 - ₹ 17,50,000

Job Opportunity

The successful candidate will be responsible for developing and implementing Language Model (LM) guardrails to prevent prompt injection, toxicity, and hallucination. This includes creating evaluation frameworks to monitor LM safety and fairness.

The ideal candidate will have 1-3 years of experience in data science, applied machine learning, or LLM-based applications. They should have strong programming skills in Python and experience writing SQL for data exploration or feature engineering.

A thorough understanding of Natural Language Processing (NLP), deep learning, and LLM architectures is essential. The candidate should also have hands-on experience with Hugging Face, LangChain, and vector stores such as FAISS and Pinecone.

Key Responsibilities:

  • Design and build LM guardrails for prompt injection protection, toxicity/bias detection, and hallucination/jailbreak identification.
  • Develop and maintain evaluation frameworks to monitor LM safety, fairness, and compliance.
  • Create automated pipelines to process, tag, and evaluate LM outputs using Python and SQL.
  • Leverage vector databases and embeddings to detect unsafe content or model drift.
  • Design internal dashboards and visualizations for POCs and internal tools.
  • Collaborate with ML engineers and product teams to integrate LM safety components into production APIs or applications.
  • Stay current with AI safety research, emerging tools, and regulatory standards.

Requirements:

  • 1-3 years of experience in data science, applied machine learning, or LLM-based applications.
  • Strong programming skills in Python and experience writing SQL for data exploration or feature engineering.
  • A solid understanding of NLP, deep learning, and LLM architectures.
  • Hands-on experience with Hugging Face, LangChain, and vector stores.
  • Familiarity with front-end basics is a plus.
  • Experience with model evaluation, red-teaming, or safety interventions in NLP/LLM systems.

This role requires the ability to thrive in a fast-paced startup environment and deploy ML pipelines in production.

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