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Salary: USD 100,000 - 130,000 / annual
iCapital is seeking an Associate AI Engineer to build and scale production-grade AI systems that deliver real business outcomes in a FinTech environment. This role is ideal for an engineer early in their career who enjoys turning ambiguous problem statements into working systems, pairing creative problem-solving with disciplined measurement, reproducibility, and operational rigor.
You will join the AI/ML team and work closely with senior AI and machine learning engineers, as well as cross-functional partners, to design, implement, evaluate, and operate AI capabilities as APIs and services. You will be expected to bring strong analytical fundamentals, curiosity, and a bias toward delivering robust, maintainable systems.
Key Responsibilities:
- Design and implement modular AI capabilities including LLM-based reasoning, document intelligence (OCR/IDP), intelligent knowledge systems, and agentic orchestration to power internal and external workflow automation.
- Own components of the full AI system life cycle to achieve tangible business outcomes, including scoping, POC, solution design, model development, deployment, monitoring, and continuous improvement in production environments.
- Build robust evaluation pipelines for AI systems, defining statistically sound, problem-specific metrics, constructing and curating benchmark datasets, and enforcing strict dataset and model versioning to ensure reproducibility and continuous improvement.
- Collaborate across teams and communicate results clearly via documentation, write-ups, and handoffs.
Workplace: In-office Monday–Thursday with remote flexibility on Friday.
Requirements:
- 2–3 years of relevant experience, including hands-on experience developing production AI/ML systems
- Exposure to AWS or cloud-native development patterns for AI/ML workloads
- Familiarity with AI development life cycle best practices
- Strong proficiency in Python
- Solid fundamentals in statistics, exploratory data analysis, and ability to reason about data quality, experiments, and error patterns
- Clear written and verbal communication with ability to document work and collaborate in a team setting
- Experience with modern LLM tooling (e.g., transformers, unsloth, vLLM, agentic frameworks) or document AI approaches (IDP)
- Familiarity with software development best practices (source control, CI/CD, testing, documentation) and working through a full development lifecycle
- Experience with various agentic communication protocols (e.g., MCP, A2A) is a plus.
