You'll work at the intersection of artificial intelligence, quantitative research, and alpha generation, building advanced systems that support high-impact decision-making across discretionary and systematic strategies.
This is a hands-on role for someone passionate about building AI tools in a real-world, fast-paced environment. While finance industry experience is preferred, the team is also open to candidates from elite engineering backgrounds (e.g. top-tier product / AI companies) who can demonstrate strong technical depth and delivery in applied AI.
What You'll Be Doing
- Design and develop AI / ML solutions to enhance investment decision-making and automate analyst workflows.
- Build tools that improve semantic search, NLP, knowledge extraction, and internal research capabilities.
- Collaborate with portfolio managers, researchers, and data engineers to translate business problems into ML use cases.
- Implement, evaluate, and optimize LLM-based solutions, leveraging vector databases and retrieval-augmented generation (RAG) techniques.
- Stay current with developments in GenAI, open-source models, and novel ML frameworks, contributing to innovation across the firm.
- Deploy models across hybrid cloud environments (on-prem and AWS), and work with tools like Kafka, Spark, and cloud-native data warehouses.
What We're Looking For
3+ years' experience in AI / ML engineering, ideally with applied work in real-time or data-intensive environments.Strong programming skills in Python.Experience with AI / ML frameworks such as PyTorch, TensorFlow, scikit-learn, or similar.Familiarity with LLM stacks and GenAI tools (e.g. LangChain, LlamaIndex, OpenAI API).Knowledge of vector databases, graph-based systems, and unstructured data processing is a plus.Background in building and scaling infrastructure in cloud environments (AWS preferred).Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related quantitative field.Experience working with hedge funds, trading firms, or financial data.Exposure to quantitative research, signal generation, or alpha discovery workflows.Understanding of secure data governance, model evaluation, and production ML operations in regulated environments.J-18808-Ljbffr