Artificial intelligence Engineer - Vice President

Artificial intelligence Engineer - Vice President

New York, New York, United States

iCapital is seeking a Vice President Artificial Intelligence Engineer to lead the design, development, and delivery of production‑grade AI systems that drive measurable business outcomes across the firm. This role is ideal for a seasoned engineer with a track record of shipping complex AI systems end‑to‑end and someone who combines deep technical expertise with strong cross‑functional partnership, architectural judgment, and is able to operate as a force multiplier for the team.

This individual will own key work streams and serve as a technical leader, driving system design, mentoring engineers, partnering directly with business stakeholders, and ensuring that AI capabilities are built to production‑grade standards of reliability, scalability, and measurability. The role is expected to bring independent judgment on technical approach, a bias toward delivery, and the ability to translate ambiguous business needs into well‑scoped, well‑executed solutions.

• Lead the architecture and delivery of production AI systems, including document intelligence (IDP), intelligent knowledge systems, and agentic orchestration, to power internal and external workflow automation at scale.
• Own AI projects end‑to‑end, from problem scoping and stakeholder alignment through solution design, implementation, deployment, monitoring, and continuous improvement, delivering tangible business outcomes with a track record of consistent, high‑quality delivery.
• Drive technical design and architectural decisions for the team, including API design, system decomposition, evaluation strategy, and infrastructure patterns, establishing standards that raise the quality bar across the AI/ML platform.
• Architect and champion robust evaluation frameworks for AI systems, defining statistically sound, problem‑specific metrics, curating benchmark datasets, and enforcing strict versioning to ensure reproducibility and continuous improvement.
• Partner directly with cross‑functional stakeholders, including the Product, Operations, Legal, and Business teams, to identify AI opportunities, translate requirements into technical plans, and communicate tradeoffs, risks, and recommendations clearly.
• Mentor and develop engineers on the team through code review, design review, pair problem‑solving, and knowledge sharing, acting as a technical role model and raising the overall capability of the group.
• Identify systemic problems and propose solutions, proactively improving team processes, tooling, and infrastructure to reduce technical debt and increase development velocity.

• 7+ years of experience developing production AI/ML systems, including hands‑on experience with AWS or cloud‑native development patterns for AI/ML workloads and a demonstrated track record of delivering complex systems from inception through production.
• Strong proficiency in Python and demonstrated ability to build well‑engineered, maintainable software, including adherence to software engineering best practices (source control, CI/CD, testing, and documentation).
• Deep expertise in at least one of the following: LLM‑based systems (fine‑tuning, inference optimization, prompt engineering, modern tooling such as transformers, vLLM, or agentic frameworks), document intelligence and IDP, or ML system design (training pipelines, model serving, evaluation infrastructure).
• Experience designing and operating end‑to‑end ML pipelines in production, including model training, deployment, monitoring, and iteration (MLOps).
• Solid fundamentals in statistics, experimentation, and data quality with the ability to reason rigorously about metrics, error patterns, and the limitations of AI systems.
• Experience leading technical design, mentoring engineers, and driving architectural decisions within a team.
• Strong written and verbal communication skills to represent the team in cross‑functional settings, document technical designs, and communicate effectively with both technical and non‑technical stakeholders.
• Experience spanning more than one of LLM systems, document intelligence, and ML…