Responsibilities
- 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
- Strong coding skills
- 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
Core Competencies
Demonstrates expertise in Natural Language Processing (NLP), Large Language Models (LLM), and generative AI techniques, with a strong foundation in machine learning and statistical analysis. Capable of designing agentic workflows and hybrid search strategies while providing technical leadership and mentoring in data science.
Tools & Technologies
- LangChain
- LangGraph
- AutoGen
- Vector Databases