Manager Data Science; LLM​/RAG​/agentic systems

We are seeking a Manager of Data Science to lead a high-impact team focused on building intelligent systems powered by LLMs and RAG architectures
. This role will focus on improving LLM-powered drafting and retrieval solutions through advanced search, embeddings, reranking, evaluation, and production-grade ML components.

You’ll drive the strategy, technical execution, and team growth necessary to build next-generation legal assistants, capable of contextual document drafting, and interaction with structured and unstructured data sources.

• Define the roadmap for applying LLMs and agentic AI to real-world legal drafting.
• 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.

• 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.

• Previous people management experience (3+ years) of teams building LLM-based applications
  , especially in domains requiring rigor and traceability like legal, finance, or compliance.
• Hands-on experience delivering systems using Lang Chain
  , LLM agent frameworks
  , vector databases
  , and prompt orchestration pipelines
  .
• Strong background in deploying retrieval-augmented generation (RAG) systems for grounded and auditable responses.
• Demonstrated success in taking LLM-powered applications from prototype to production.

• Proficient with Python and LLM tooling:
  Lang Chain, Hugging Face Transformers, OpenAI APIs, and prompt tuning techniques.
• Familiarity with vector databases (e.g., Solr, Elasticsearch, Qdrant, Weaviate), knowledge graphs, and hybrid retrieval architectures.
• Working knowledge of containerization (Docker, Kubernetes), CI/CD, and model serving tools (Torch Serve, Triton, Ray Serve).
• Cloud infrastructure experience on AWS, Azure, or GCP.

• Graduate degree in Computer Science, AI, Machine Learning, or equivalent experience.
• 6 years of post-degree experience, with 3+ years in a data science or applied AI leadership role, with a focus on NLP/LLM systems.
• Prior experience in legal tech, legal AI, or document-intensive domains is highly desirable.
• Familiarity with ethical/legal considerations in deploying generative AI in professional settings.