Director, AI Engineering

Job Title: Director, AI Engineering

Location: New York, NY (Hybrid – 3 Days In Office)

About The Role This is a hybrid role requiring 3 days per week in office in New York, NY. Limited travel of less than 10% may be required for events and vendor meetings.

Job Summary As a Director, AI Engineering within the Artificial Intelligence & Data organization, you will be a senior technical leader driving enterprise AI transformation. This is a hands-on, full-stack AI role: you design, build, and deliver AI solutions end to end across traditional ML, Generative AI, and Agentic AI, while shaping technical strategy and mentoring a growing team. You will scope and deliver core AI solutions in close partnership with business stakeholders, data scientists, engineers, and product and technology partners, and help shape enterprise AI practices and standards. You will own solution delivery on top of the enterprise AI platform, shipping agentic solutions end to end rather than building underlying platform infrastructure, and partner with the business to assess and quantify the overall value delivered by AI solutions.

Key Responsibilities

• Lead AI/ML and GenAI initiatives end-to-end in partnership with data, technology, product, and business teams, from opportunity identification and feasibility assessment through solution design, delivery, and stakeholder alignment.
• Own end-to-end delivery for AI solutions from build through evaluation, deployment, and production monitoring, shipping and operating them on the enterprise AI platform using its existing CI/CD, IaC templates, and gateway.
• Design and implement agentic AI systems including multi-agent orchestration, tool use, memory architectures, human-in-the-loop checkpoints, and safety guardrails; build multi-agent solutions with sub-agents that plan, implement, validate, deploy, and log.
• Design and deploy production-grade RAG and retrieval systems including hybrid search, reranking, evaluation, and advanced retrieval patterns such as agentic RAG and graph-enhanced retrieval.
• Lead technical decisions across the AI stack: model selection, orchestration frameworks and tool-integration protocols (e.g., LangGraph, MCP, or direct API integration), cloud AI platforms (GCP, AWS), and Databricks integration.
• Rapidly prototype AI-powered applications and interfaces to validate ideas, test usability, and accelerate adoption using modern frameworks such as Streamlit or React.
• Build with AI-assisted engineering tools daily as a force multiplier, with critical review of all generated output; build engineering agents that accelerate the team’s own delivery, such as agents that assist with testing, code review, or deployment validation.
• Define and evolve technical standards for AI development including evaluation frameworks, testing practices, observability, and responsible AI principles; evaluate and integrate emerging AI capabilities.
• Provide technical leadership and mentorship to data scientists and AI engineers, fostering a culture of rapid experimentation, rigorous evaluation, and continuous learning.

Requirements

• Advanced degree (MS or PhD) in Computer Science, AI, Engineering, Mathematics, or a related quantitative field.
• 8+ years of experience applying data science, ML, and AI to real-world business problems, with progressive growth in scope, complexity, and influence.
• Fluency with AI-assisted engineering tools (e.g., Codex, Claude Code) used daily as a force multiplier, with critical review of generated output; experience building engineering agents that accelerate delivery itself.
• Full-stack AI fluency: demonstrated ability to work across statistical modeling and ML through LLM application development and agentic system design, including rapid prototyping and owning solutions through to production.
• Strong software engineering skills in Python; working knowledge of SQL and JavaScript/TypeScript sufficient to build and ship a solution’s front end (e.g., React); comfort with modern development practices including testing, code review, and CI/CD; working fluency with Docker and Kubernetes-based deployment.
• Deep hands-on experience with LLMs, RAG architectures, and prompt engineering, including graph-based retrieval (GraphRAG, knowledge graphs) and multi-agent and sub-agent orchestration frameworks (e.g., LangGraph, Google ADK) and MCP or equivalent tool-integration protocols for production agentic systems.
• Exper…