Director, Applied AI

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Director, Applied AI based in United States. This role leads the team responsible for the intelligence that powers AI agents and decision-making systems across a B2B data environment.

You will own the data graph strategy end to end, spanning training data, machine learning, LLMs, agentic systems, and model serving.

The position combines technical leadership with hands-on engineering, prototyping, experimentation, and production delivery.

You will guide a small, senior team while setting a high technical bar for model quality, evaluation, scalability, and reliability.

The role offers broad cross-functional exposure, partnering with product, platform, security, legal, and executive stakeholders.

You will also shape how AI development tools are used across the team, balancing speed of execution with rigorous engineering standards.

This is an opportunity to influence foundational AI capabilities while developing senior technical talent and delivering measurable production impact.

Accountabilities

• Lead the strategy and development of a comprehensive B2B data graph, extending coverage to organizations with limited public information.
• Own the end-to-end data graph lifecycle, from training data and modeling through production model serving.
• Develop systems that enable AI agents to reason over accurate information about companies, people, relationships, and purchasing activity.
• Lead initiatives around agent memory, clearly distinguishing user-provided information from data already maintained by authoritative systems.
• Select the most appropriate technical approach for each problem, including classical machine learning, language models, agentic systems, and code-based solutions.
• Use measured evidence to prioritize high-value initiatives and discontinue approaches that are unlikely to deliver sufficient impact.
• Establish evaluation standards for machine learning models and AI agents, ensuring quality claims are measurable and trustworthy.
• Build evaluation datasets, regression gates, experiment frameworks, and validation processes for continuous model improvement.
• Own inference cost, latency, and capacity alongside model quality and system performance.
• Make informed build-versus-buy decisions and assess opportunities for model distillation and optimization.
• Hire, mentor, and develop machine learning engineers, data scientists, and research engineers.
• Help senior engineers grow into technical leadership roles while maintaining a strong engineering culture.
• Remain hands-on by writing production code, developing prototypes, and working directly alongside the engineering team.
• Establish standards for the effective use of agentic coding tools, combining precise specifications with rigorous code review.
• Collaborate with product, platform, security, legal, and other stakeholders to align technical work with broader business and operational requirements.
• Communicate technical results, limitations, trade-offs, and risks clearly to executive stakeholders.
• Set and maintain a high technical bar for production machine learning, data science, AI agents, and supporting infrastructure.

Requirements

• Significant demonstrated experience building and shipping production machine learning systems, with hands-on technical leadership.
• Proven experience hiring, mentoring, and developing senior machine learning engineers and data scientists.
• Current hands-on engineering experience, including writing code, building prototypes, and contributing directly to production systems.
• Strong expertise in classical machine learning and data science, including supervised learning, feature engineering, statistical inference, and experiment design.
• Strong SQL skills and the ability to work effectively with complex datasets and data-intensive systems.
• Production experience with LLMs and agentic AI systems, including the judgment to determine when these approaches are appropriate.
• Experience establishing rigorous model evaluation standards, including leakage-safe validation and calibration.
• Demonstrated ownership of inference cost, latency, capacity, and operational considerations alongside model quality.
• Experience making build-versus-buy decisions for machine learning and AI capabilities.
• Strong ability to communicate technical fi…