Assistant Vice President AI Engineer

Who you are

• The ideal candidate is an experienced, hands-on 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 able to operate as a force multiplier for the team
• 5+ years of experience developing and deploying production AI/ML systems, including cloud-native solutions on AWS or similar platforms, with a 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 the 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 and tooling
• 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 contributing to technical architecture and design discussions, conducting code and design reviews, and mentoring or supporting the growth of other engineers
• Excellent written and verbal communication skills, with the ability to document technical solutions, partner effectively with cross-functional stakeholders, and communicate complex concepts to both technical and non-technical audiences
• Experience in financial services or FinTech, particularly within document-heavy, regulated, or compliance-sensitive environments is preferred
• Contributions to open-source projects, technical publications, conference presentations, patents, or other demonstrated thought leadership in applied AI and machine learning is preferred

What the job involves

• ICapital is seeking an Assistant Vice President Artificial Intelligence Engineer to design, develop, and deliver production-grade AI systems that drive measurable business outcomes across the firm. This role is expected to bring sound judgment on technical approach, a bias toward delivery, and able to translate ambiguous business needs into well-scoped, well-executed solutions
• This individual will own AI projects and defined workstreams, contribute to system design and technical decisions, partnering directly with business stakeholders, and ensuring that AI capabilities are built to production-grade standards of reliability, scalability, and measurability
• Design and deliver production AI systems, including document intelligence (IDP), intelligent knowledge systems, agentic orchestration, conversational AI, and generative AI applications, to power internal and external business processes, digital experiences, 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 with a track record of consistent, high-quality delivery
• Contribute to technical design and architectural decisions for the team, including API design, system decomposition, evaluation strategy, and infrastructure patterns, following and contributing to team engineering standards across the AI/ML platform
• Develop and apply 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
• Support and mentor junior 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