Artificial intelligence Engineer - Vice President

About The Role

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 the ability 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.

• 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.
• 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 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 AI 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 platform and infrastructure.
• Familiar with agentic architectures and protocols (e.g., MCP and A2A) or designing multi-step, tool-using AI workflows.
• Knowledge of cost and latency optimization for LLM inference at scale (quantization, batching strategies, and model routing).
• Prior experience in financial services or Fin Tech, particularly…