Responsibilities
- Develop and implement NLP, LLM, and generative AI approaches (e.g., RAG, prompt strategies, patent search).
- Define agentic workflows and reasoning strategies for multi-step IP tasks.
- Develop retrieval strategies, including hybrid search (semantic + lexical), and evaluation metrics (e.g., relevance, ranking quality).
- Analyse large-scale IP 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, IP experts, and engineers to align solutions with user needs.
Requirements
- Degree 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 in Python.
- 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., Google ADK, LangChain, LangGraph, AutoGen) in real world use cases.
- Proficiency in data analysis tools.
- Strong foundation in statistics, modeling, and large-scale text processing.
Core Competencies
Demonstrates expertise in developing and implementing NLP, LLM, and generative AI approaches, with a strong foundation in machine learning and data analysis. Proficient in designing agentic workflows and hybrid search strategies to enhance model performance and deliver actionable insights.