Key responsibilities:

Develop cutting-edge AI solutions for real-world legal technology challenges, focusing on innovative applications and improvements.
Conduct independent research to explore new AI methods, attend industry conferences, and establish frameworks for scientific experimentation.
Build and optimize LLM-based workflows (e.g., Langchain, AutoGen) to ensure models are transparent and interpretable for legal use cases.
Design and implement robust research plans to evaluate the effectiveness of NLP and AI systems in legal applications.
Utilize natural language processing (NLP) to extract specific information from large, unstructured, multi-domain, and multilingual legal documents, with a focus on privacy and security.
Write high-quality, scalable, and maintainable code for internal applications, such as document drafting, data extraction, and labeling, adhering to best practices.
Collaborate with legal practice teams, data scientists, and data engineers to create and deploy NLP and AI-based solutions, ensuring alignment with ethical AI practices and compliance with legal regulations.
Set up and manage model monitoring systems to maintain adherence to legal and ethical standards.

The ideal candidate will have:

5+ years of commercial experience in data science with a strong focus on NLP projects, especially document analysis.
Advanced skills in Python and deep learning frameworks like PyTorch.
Proficiency in NLP libraries such as Langchain, Langraph, LlamaIndex, and Hugging Face Transformers.
Proven experience with large language models (LLMs) and transformer architectures (e.g., Llama3, Gemini), including advanced prompt engineering and fine-tuning techniques like LoRa and QLoRa.
Knowledge of LLMOps, including deploying, monitoring, and maintaining AI solutions.
Experience with Azure cloud infrastructure (Azure AI, Azure Machine Learning, Azure Cognitive Services). GCP Vertex AI knowledge is a plus.
Skilled in using Azure DevOps for version control and project management.