AI Engineer (Multi-Agentic Quant Systems)

Role Overview

We are seeking a Junior AI Engineer to join our multi-agent systems team. You will be responsible for designing, implementing, and maintaining AI-driven agent workflows that support our quantitative research, backtesting engines, and live trading infrastructure. This is a hands-on role where you’ll work closely with senior engineers and quant researchers to turn experimental ideas into production-ready agents that generate measurable alpha.

Key Responsibilities

• Develop and maintain multi-agent AI pipelines for feature generation, factor evaluation, and strategy testing.
• Implement autonomous agent behaviors for data ingestion, strategy induction, and trade log analysis.
• Contribute to the integration of LLM-based reasoning into trading workflows (e.g., factor discovery, market regime detection).
• Collaborate with quants to translate trading hypotheses into agentic workflows, ensuring they are quantifiable and testable.
• Optimize system performance across real-time data streams, distributed backtesting, and live trading engines.
• Participate in code reviews, testing, and debugging to ensure robustness and reliability of AI-driven agents.
• Document designs, experiments, and learnings to accelerate iteration cycles.

Requirements

• Bachelor’s degree in Computer Science, AI/ML, Data Science, or a related field.
• Strong proficiency in Python or C++ (bonus: both).
• Familiarity with agentic AI frameworks (LangChain, AutoGen, CrewAI, etc.) or strong willingness to learn.
• Knowledge of machine learning concepts, reinforcement learning, or multi-agent systems.
• Exposure to quantitative finance, trading systems, or market data processing is a plus.
• Ability to write clean, modular, and well-documented code.
• Strong problem-solving mindset and bias toward rapid experimentation.

Nice-to-Haves

• Experience with LLMs, prompt engineering, or vector databases (e.g., Pinecone, Postgres w/ pgvector).
• Knowledge of numerical computing (NumPy, pandas, PyTorch, TensorFlow).
• Familiarity with distributed systems and DAG orchestration tools (Airflow, Prefect, Ray).
• Understanding of financial market structure, small-cap equities, or alternative asset classes (crypto, options, futures).

What We Offer

• Opportunity to work at the frontier of AI x Quant trading, building systems that redefine what’s possible.
• Fast-paced, iterative environment where ideas move quickly from prototype to production.
• Mentorship from seasoned quant traders and AI engineers.
• Competitive compensation and performance-based upside.
• Exposure to multi-asset, multi-strategy trading pipelines and real-world alpha generation.