Artificial Intelligence / Machine Learning Engineer - Associate

iCapital•New York, NY

3d•$100,000 - $130,000•Hybrid

About The Position

iCapital is seeking an Associate AI Engineer to help build and scale production-grade AI systems that deliver business outcomes. The role is intended for an early-career engineer who can turn ambiguous problem statements into working systems and combine creative problem-solving with disciplined measurement, reproducibility, and operational rigor. As part of the AI/ML team, the engineer will work closely with senior AI and machine learning engineers and cross-functional partners to design, implement, evaluate, and operate AI capabilities as APIs and services in a FinTech environment. The role involves building modular AI capabilities, owning components across the AI system life cycle, and developing evaluation processes that support robust, maintainable systems and continuous improvement.

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 and familiarity with AI development life-cycle best practices.
Strong proficiency in Python.
Solid fundamentals in statistics and exploratory data analysis, with the ability to reason about data quality, experiments, and error patterns.
Clear written and verbal communication skills, including the ability to document work and collaborate in a team setting.
Familiarity with software development best practices, including source control, CI/CD, testing, and documentation, and experience working through a full development lifecycle.

Nice To Haves

Experience with modern LLM tooling such as transformers, unsloth, vLLM, or agentic frameworks, or with document AI approaches (IDP).
Experience with agentic communication protocols such as MCP or A2A.

Responsibilities

Design and implement modular AI capabilities, including LLM-based reasoning, document intelligence (OCR/IDP), intelligent knowledge systems, and agentic orchestration for workflow automation.
Own components of the AI system life cycle, including scoping, proof of concept, solution design, model development, deployment, monitoring, and continuous improvement in production environments.
Build evaluation pipelines by defining statistically sound, problem-specific metrics and constructing and curating benchmark datasets.
Enforce dataset and model versioning to support reproducibility and continuous improvement.
Collaborate across teams and communicate results through documentation, write-ups, and handoffs.