Artificial Intelligence Engineer - Associate

iCapital•Greenwich, CT

4h•Hybrid

About The Position

iCapital is seeking an Associate AI Engineer to help build and scale production-grade AI systems that deliver real business outcomes. This role is ideal for an engineer early in their career who enjoys turning ambiguous problem statements into working systems, and who pairs creative problem-solving with disciplined measurement, reproducibility, and operational rigor. This individual will be on the AI/ML team and will 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 in a real-world FinTech environment. This individual will be expected to bring strong analytical fundamentals, curiosity, and a bias toward delivering robust, maintainable systems.

Requirements

2-3 years of relevant experience, including hands-on experience developing production AI/ML systems, including exposure to AWS or cloud-native development patterns for AI/ML workloads, and familiarity with AI development life cycle best practices
Strong proficiency in Python
Solid fundamentals in statistics, exploratory data analysis, and the ability to reason about data quality, experiments, and error patterns
Clear written and verbal communication with the ability to document work and collaborate in a team setting
Experience with modern LLM tooling (i.e. transformers, unsloth, vLLM, agentic frameworks) or document AI approaches (IDP)
Familiar with software development best practices (source control, CI/CD, testing, documentation) and working through a full development lifecycle

Nice To Haves

Experience with various agentic communication protocols, (i.e. MCP, A2A) is a plus

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.