Define the roadmap for applying LLMs and agentic AI to real-world legal drafting, summarization, and task automation challenges.
Evangelize and embed AI-driven change across legal product platforms.
Translate evolving generative AI capabilities into tangible customer value within the legal domain.
Architect systems that combine foundation models with RAG pipelines to draft contracts, memos, and pleadings grounded in enterprise knowledge.
Guide the development of AI agents that perform multi-step reasoning, cite evidence, retrieve domain-specific content, and produce compliant outputs.
Lead evaluation of model performance for generation, summarization, classification, and dialogue-based workflows with legal constraints in mind.
Build scalable prompt engineering frameworks and manage fine-tuning or adaptation of models to legal datasets.
Mentor and grow a multidisciplinary team of LLM-focused Data Scientists and ML Engineers.
Drive cross-functional collaboration with Legal SMEs, Data Engineers, Product Managers, and Design.
Establish best practices for evaluation, observability, and responsible use of generative AI.
Oversee development of infrastructure to support continuous delivery and monitoring of LLM systems in production environments. Required Qualifications
3+ years of people management experience leading teams building LLM-based applications, particularly in domains requiring rigor and traceability such as legal, finance, or compliance.
Hands-on experience delivering systems using LangChain, LLM agent frameworks, vector databases, and prompt orchestration pipelines.
Strong experience deploying retrieval-augmented generation (RAG) systems for grounded and auditable responses.
Demonstrated success taking LLM-powered applications from prototype to production.
Proficiency in Python and LLM tooling, including LangChain, HuggingFace Transformers, OpenAI APIs, and prompt tuning techniques.
Familiarity with vector databases such as Solr, Elasticsearch, Qdrant, and Weaviate.
Knowledge of knowledge graphs and hybrid retrieval architectures.
Working knowledge of Docker, Kubernetes, CI/CD, and model serving tools such as TorchServe, Triton, or Ray Serve.
Experience with cloud infrastructure on AWS, Azure, or GCP.
Strong leadership, communication, collaboration, and problem-solving skills. Preferred Qualifications
Graduate degree in Computer Science, AI, Machine Learning, or equivalent experience.
10+ years of post-degree experience, with 4+ years in a data science or applied AI leadership role focused on NLP/LLM systems.
Prior experience in legal technology, legal AI, or document-intensive domains.
Familiarity with ethical and legal considerations when deploying generative AI in professional settings.

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Senior Manager of Data Science