Artificial Intelligence Engineer (Vice President)

Join ICapital as a Vice President Artificial Intelligence Engineer, where you will lead the design, development, and delivery of production-grade AI systems that drive measurable business outcomes. This role requires a seasoned engineer with a track record of shipping complex AI systems end-to-end, strong cross-functional partnership, and architectural judgment. You will own key workstreams, serve as a technical leader, and partner directly with business stakeholders.

• Lead the architecture and delivery of production AI systems, including document intelligence and intelligent knowledge systems.
• Own AI projects end-to-end, from problem scoping and stakeholder alignment through solution design, implementation, deployment, monitoring, and continuous improvement.
• Drive technical design and architectural decisions for the team, including API design, system decomposition, evaluation strategy, and infrastructure patterns.
• Prior experience in financial services or FinTech, particularly in document-heavy or compliance-sensitive domains
• 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
• Experience spanning more than one of LLM systems, document intelligence, and ML platform and infrastructure
• Knowledge of cost and latency optimization for LLM inference at scale (i.e. quantization, batching strategies, and model routing)
• Strong proficiency in Python and demonstrated ability to build well-engineered, maintainable software, including adherence to software engineering best practices (i.e. source control, CI/CD, testing, and documentation)
• Familiar with agentic architectures and protocols (i.e. MCP andA2A) or designing multi-step, tool-using AI workflows
• Experience leading technical design, mentoring engineers, and driving architectural decisions within a team
• Deep expertise in at least one of the following: LLM-based systems (fine-tuning, inference optimization, prompt engineering, modern tooling AI tooling, such as transformers, vLLM, or agentic frameworks), document intelligence and IDP, or ML system design (training pipelines, model serving, evaluation infrastructure)
• Contributions to open-source projects or published technical writing demonstrating thought leadership in applied AI
• 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 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