Advanced AI Safety Engineer

2 days ago

Chennai, Tamil Nadu, India beBeeLlm Full time ₹ 15,00,000 - ₹ 25,00,000
Expert LLM Safety Specialist

Key Responsibilities

  • Design and Develop Effective LLM Guardrails
  • Build, Maintain and Enhance Evaluation Frameworks for LLM Safety, Fairness, and Compliance
  • Develop Automated Pipelines to Process, Tag, and Evaluate LLM Outputs using Python and SQL
  • Leverage Vector Databases and Embeddings to Detect Unsafe Content or Model Drift
  • Create Internal Dashboards and Visualizations for POCs and Internal Tools
  • Collaborate with ML Engineers and Product Teams to Integrate LLM Safety Components into Production APIs or Applications
  • Stay Current with AI Safety Research, Emerging Tools and Regulatory Standards

Required Skills and Qualifications

  • 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
  • Solid Understanding of NLP, Deep Learning and LLM Architectures
  • Hands-on Experience with Hugging Face, LangChain, LLM APIs and Vector Stores
  • Familiarity with Front-end Basics is a Plus, Not Mandatory
  • Experience with Model Evaluation, Red-teaming, or Safety Interventions in NLP/LLM Systems
  • Familiarity with Deploying ML Pipelines in Production and Ability to Thrive in a Fast-paced Startup Environment is Preferred

The ideal candidate will have a strong background in data science, applied machine learning and large language model-based applications. They should be proficient in programming languages such as Python and have experience writing SQL for data exploration or feature engineering.

A solid understanding of natural language processing, deep learning and large language model architectures is also essential. Additionally, hands-on experience with tools such as Hugging Face, LangChain and vector stores is required.

The role requires collaboration with machine learning engineers and product teams to integrate large language model safety components into production APIs or applications. Therefore, the ability to thrive in a fast-paced startup environment and deploy machine learning pipelines in production is preferred.

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