Develop and implement NLP, LLM, and generative AI approaches (e.g., RAG, prompt strategies)
Define agentic workflows and reasoning strategies for multi-step legal tasks
Develop retrieval strategies, including hybrid search (semantic + lexical), and evaluation metrics (e.g., relevance, ranking quality)
Analyze large-scale legal datasets to extract insights and improve model performance
Establish best practices for model evaluation, validation, and benchmarking
Translate experimental results into clear product recommendations and business impact
Collaborate with product, legal experts, and engineers to align solutions with user needs
Mentor team members and provide technical leadership in data science and AI

Requirements

Master’s degree or above in a quantitative or technical field (Statistics, Computer Science, Mathematics, Data Science, etc.)
Strong experience in machine learning, NLP, and LLM-based modeling
Strong experience designing and running experiments, including model evaluation and iteration
Experience with generative AI techniques (e.g., prompt engineering, RAG)
Experience designing and evaluating hybrid search (semantic + lexical) using embeddings and vector databases
Experience designing agentic workflows and reasoning strategies, with hands-on experience applying agent frameworks (e.g., LangChain, LangGraph, AutoGen) in real-world use cases
Proficiency in Python and data analysis tools
Strong foundation in statistics, modeling, and large-scale text processing

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

NLPLLMgenerative AImachine learningmodel evaluationhybrid searchdata analysisstatisticslarge-scale text processingprompt engineering

Soft Skills

technical leadershipmentoringcollaborationcommunication

Certifications

Master’s degree