Director of Artificial Intelligence

Core Responsibilities

• Design and ship agentic AI systems.
  • Architect, build, and operate agentic AI applications; systems that plan, call tools, retrieve and act on information, and execute multi-step workflows with appropriate human oversight.
  • Build and maintain the orchestration layer (tool/function calling, multi-agent coordination, memory, state management, retries, and guardrails), and integrate agents with firm systems via MCP (Model Context Protocol) servers and other tool interfaces.
  • Define where agents operate autonomously versus where a human stays in the loop.
  • Develop production-grade pipelines and tools that collect and process information from diverse sources including public and subscription websites, REST and streaming API feeds, MCP servers and feeds, email systems, document management systems, and SQL and vector databases.
  • Own these systems end to end, including retrieval (RAG) architectures, evaluation, observability, and iteration.
  • Model selection and orchestration.
    • Demonstrate working fluency with frontier foundation models (e.g., OpenAI, Anthropic Claude, Google) via API, as well as locally hosted open-weight models (e.g., Llama, Mistral, Qwen).
    • Make sound, cost-aware decisions about which model and which agent design fit each use case, and route tasks accordingly.
    • Tune and operate open-weight models.

Required Qualifications

• Minimum 5 years of technology experience, including a minimum 3 years building and deploying production AI/ML applications.
• Candidates should be prepared to discuss systems they have personally built.
• Demonstrated experience building agentic AI systems.
• Tool/function calling, multi-step or multi-agent orchestration, and integrating agents with external systems and data sources (including MCP).
• Strong software engineering skills, including Python, working with REST/streaming APIs, SQL and vector databases, and modern AI/LLM and agent frameworks.
• Hands-on experience with RAG pipelines, embeddings, evaluation, and observability for LLM and agent applications.
• Working knowledge of utilizing open-weight models, including on-premises or private-cloud GPU infrastructure.
• Demonstrated experience designing AI systems under strict security, privacy, and confidentiality constraints; familiarity with data-leakage prevention, least-privilege tool access, encryption, and audit logging.
• Working knowledge of AI governance and risk frameworks (e.g., ISO/IEC 42001, NIST AI RMF) and relevant data-privacy regulation.
• Proven ability to collaborate effectively with security, IT, and non-technical stakeholders, and to communicate technical concepts to attorneys and firm leadership.

Preferred Qualifications

• Prior experience in a law firm, legal-technology provider, or other regulated professional-services or highly confidential environment.
• Understanding of attorney-client privilege, work-product doctrine, and the legal and ethical duties (e.g., ABA Model Rules and recent formal opinions on generative AI) that constrain how AI may be used in legal practice.
• Experience building MCP servers and designing evaluation/guardrail frameworks for autonomous agents.
• Familiarity with legal-specific platforms and use cases (document review, contract analysis, legal research, drafting).
• Background that combines software/AI engineering with exposure to litigation, eDiscovery, or knowledge management.
• Degree in computer science, engineering, data science, or related field; advanced degree or JD a plus but not required in lieu of practical engineering experience.