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Salary: USD 170,000 - 210,000 / annual
iCapital is seeking a Vice President AI Engineer to lead the design, development, and delivery of production-grade AI systems that drive measurable business outcomes across the firm. This role combines deep technical expertise with cross-functional leadership, requiring someone who can architect complex AI solutions, mentor engineers, and partner directly with business stakeholders to translate ambiguous requirements into well-scoped, high-quality deliverables.
Key Responsibilities:
- Lead architecture and delivery of production AI systems including document intelligence (IDP), intelligent knowledge systems, agentic orchestration, conversational AI, and generative AI applications powering internal and external business processes and 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 quality across the AI/ML platform.
- Architect and champion robust evaluation frameworks for AI systems, defining statistically sound 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 and risks.
- Mentor and develop engineers through code review, design review, pair problem-solving, and knowledge sharing, acting as a technical role model.
- Identify systemic problems and propose solutions to improve team processes, tooling, and infrastructure, reducing technical debt and increasing development velocity.
Work Environment:
Employees work in the office Monday-Thursday with flexibility to work remotely on Friday. The role includes equity for all full-time employees and an annual performance bonus.
Requirements:
- 7+ years of experience developing and deploying production AI/ML systems, including cloud-native solutions on AWS or similar platforms, with proven track record of delivering complex applications from design through production.
- Strong proficiency in Python and software engineering best practices including source control, CI/CD, testing, documentation, and development of scalable, maintainable software.
- Deep expertise building production AI solutions including LLM applications, AI agents, retrieval-augmented generation (RAG), conversational AI, document intelligence, and agentic workflows, with hands-on experience using modern AI frameworks, tooling, and protocols such as MCP and A2A.
- Experience designing, deploying, and operating end-to-end machine learning pipelines including model training, deployment, monitoring, evaluation, and continuous improvement in production environments.
- Strong foundation in statistics, experimentation, data quality, and AI system evaluation, with experience developing benchmarks, defining performance metrics, analyzing errors, and optimizing systems for accuracy, reliability, scalability, cost, and latency.
- Experience leading technical design and architectural decisions, conducting code and design reviews, mentoring engineers, and driving engineering excellence across teams.
- Excellent written and verbal communication skills with ability to document technical solutions, collaborate with cross-functional stakeholders, and communicate complex concepts to both technical and non-technical audiences.
- Experience spanning multiple AI domains including LLM systems, document intelligence, and ML platforms or infrastructure.
- Preferred: Experience in financial services or FinTech, particularly within document-heavy, regulated, or compliance-sensitive environments.
- Preferred: Contributions to open-source projects, technical publications, conference presentations, patents, or other demonstrated thought leadership in applied AI and machine learning.
