AI Engineer

Hedge Fund; Chicago (IL); On-site; USD 130,000 - 170,000; Full time

Job summary

Hedge Fund is seeking an elite AI Engineer to join the Quantitative Research team. This position involves architecting an AI platform to enhance investment workflows, developing machine learning tools, and conducting in-depth quantitative research. Ideal candidates should have a PhD or MSc in relevant fields along with 5-10 years of experience in asset management or hedge funds. Proficiency in Python and expertise in modern AI and machine learning systems is essential.

Qualifications

• 5 to 10 years of direct experience in a Quantitative Research or Analyst capacity.
• Experience with stochastic calculus, derivatives pricing, and portfolio theory.
• Expert-level programming skills in Python with experience in machine learning frameworks.

Responsibilities

• Architect and build a proprietary AI platform for quantamental research.
• Develop machine learning techniques to enhance portfolio construction.
• Research and deploy systematic signals leveraging machine learning.

Skills

AI Architecture

Financial Engineering

Communication

Software Development

Education

PhD or MSc in Financial Engineering, Applied Mathematics, Statistics, Physics, or Computer Science

Tools

Python

C++

Java

PyTorch

TensorFlow

Scikit-Learn

SQL

Docker

Azure

Spark

Dask

Job description

Asset Manager is seeking an elite AI Engineer with a strong foundation in software engineering and AI to join our Quantitative Research team. This role focuses on the implementation of cutting‑edge Machine Learning and AI methods directly into investment workflows. Partnering with Analysts, Portfolio Managers, Traders, and senior risk takers across the firm, you will translate their workflows into a 0‑to‑1 build of an AI platform to enhance and scale alpha generation and risk mitigation strategies. As a leader and architect of AI solutions, you will act as the critical bridge between frontier AI capabilities and commercial, high‑conviction investment decision‑making.

Responsibilities

• AI Platform Development: Architect and build a proprietary AI platform from the ground up to support quantamental research, enabling scalable data integration, model development, and signal generation across investment teams.
• AI & ML Tooling: Develop machine learning techniques and AI tools to enhance portfolio construction and risk models to identify and react to emerging risks and changes in correlations.
• Alpha Generation: Research, back‑test, and deploy systematic signals leveraging both traditional financial engineering techniques and advanced machine learning (e.g., deep learning, LLMs for sentiment analysis, alternative data extraction).
• Stakeholder Management: Serve as the primary liaison between the AI and Quantitative Research team and investment teams. Embed directly with Analysts and PMs to identify solutions that increase productivity and enhance investment performance.

Core Competencies

• AI Architecture: Familiarity with emerging agentic AI patterns, including multi‑agent workflows and RAG systems, with the ability to translate these into scalable enterprise solutions.
• Financial Engineering: Experience with building solutions for tools that leverage stochastic calculus, derivatives pricing, time‑series econometrics, and portfolio theory.
• Communication: Demonstrated ability to speak with analysts, PMs and traders, and translate business needs to algorithmic solutions.
• Software Development: Ability to write clean, scalable, and highly optimized code and leverage agents for productivity enhancement.

Education & Industry Experience

• Required Education
  • PhD (strongly preferred) or MSc in Financial Engineering, Applied Mathematics, Statistics, Physics, or Computer Science from a top‑tier institution.
• Experience
  • 5 to 10 years of direct experience in a Quantitative Research or Analyst capacity within a top‑tier Asset Management firm, Hedge Fund, or Proprietary Trading desk.

Technical Qualifications

• Programming: Expert‑level Python (NumPy, Pandas, SciPy). Proficiency in C++ or Java for performance‑critical components is a plus.
• Machine Learning & Modern AI Systems: Deep practical experience with PyTorch, TensorFlow, and Scikit‑Learn, along with hands‑on experience in Retrieval‑Augmented Generation, vector databases, and agent‑based frameworks (e.g., LangChain, Hugging Face Transformers) for building context‑aware LLM applications.
• Data & Systems: Proficiency in SQL and handling large‑scale unstructured/alternative datasets. Experience with cloud infrastructure (Azure) and distributed computing (Spark, Dask).
• Software Engineering: Strict adherence to CI/CD, Git version control, Docker containerization, and Agile methodologies.