AI Engineer
About The Position
As a Quantitative Technologist, the AI Engineer enhances live trading systems, research infrastructure, and alpha generation through technology innovation. The firm uses cutting-edge software and hardware, mostly modern C++, to deliver production-level code daily and develop automated strategies, alpha-generation processes, live trading systems, and machine-learning research and simulation infrastructure used to analyze petabytes of data. The role builds and accelerates the firmwide AI ecosystem and infrastructure, identifies opportunities to introduce AI-first systems and automation into developer and researcher workflows, and designs, deploys, and evaluates production-grade AI applications and agentic workflows. It also develops platform-level safeguards and optimizes the quality, accuracy, speed, cost, safety, and user experience of agent systems.
Requirements
- At least 5 years of overall technical experience, including at least 2 years of hands-on experience designing and building Generative AI/LLM applications in production.
- Bachelor’s, Master’s, or PhD in Computer Science, Data Science, Machine Learning, or a related technical field.
- Strong proficiency in Python, sound software engineering principles, and a deep understanding of the full AI/ML lifecycle, from data preparation through observability.
- Expert familiarity with agentic frameworks and modern AI orchestration and development tools, such as LangChain, LangGraph, AutoGen, and LlamaIndex, alongside practical prompt-engineering skills.
- Ability to evaluate and select foundational or open-weight models, balancing enterprise trade-offs across capability, context length, hosting, security, and cost.
Responsibilities
- Build and accelerate the firmwide AI ecosystem and infrastructure to support AI-native systems and tooling.
- Identify workflow opportunities for developers and researchers to introduce AI-first systems and automation.
- Design, deploy, and evaluate production-grade AI applications and advanced agentic workflows using RAG, semantic search, planning, and structured outputs.
- Develop platform-level safeguards, including evaluation harnesses, guardrails, approval workflows, tool-use limits, source attribution, and kill switches.
- Optimize agent-system metrics, including reasoning quality, retrieval accuracy, latency, cost, safety, and user experience.
Benefits
- Competitive base salary.
- Quarterly bonus based on individual performance and contribution toward the success of others and the firm.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
