What You'll Do:
• Build Generative AI applications. Design and deploy LLM-powered systems, including retrieval-augmented generation (RAG), intelligent agents, and multi-step agentic workflows that automate complex research and analytical tasks.
• Develop quantitative and predictive models. Create forecasting, ranking, and predictive models from structured and unstructured data to support investment research, portfolio management, and risk analysis.
• Take solutions to production. Build scalable software and data pipelines, and set up evaluation, monitoring, observability, and governance so models stay reliable and deliver measurable business value.
• Partner with the business. Work with investment, operation, and distribution teams to turn business problems into practical, well-scoped AI solutions.
• Advance the firm's AI capabilities. Evaluate emerging research and tools, prototype promising approaches, and lead their adoption across the organization.
What We're Looking For:
Required Qualifications
• Bachelor's degree in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, Engineering, or a related quantitative field.
• 2+ years of experience building and deploying machine learning or AI solutions in production.
• Strong Python skills and experience writing production-grade, well-tested code.
• Solid grounding in machine learning, statistical modeling, and predictive analytics.
• Hands-on Generative AI experience, including LLMs, embeddings, retrieval systems, prompt engineering, and LLM evaluation.
• Experience designing agentic or multi-agent systems using tool calling, API integrations, Model Context Protocol (MCP), and orchestration frameworks (e.g., LangGraph, LlamaIndex, or similar).
• Clear communication skills, including the ability to explain technical trade-offs to non-technical stakeholders.
Preferred Qualifications
• Master's degree or PhD in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, Engineering, or a related quantitative field.
• Experience applying AI or quantitative methods in asset management, financial services, fintech, or capital markets.
• Working knowledge of investment research, portfolio management, financial modeling, or risk management.
• Experience with financial text data such as company filings, earnings call transcripts, news, and research reports.
• Familiarity with cloud platforms (AWS, Azure, or GCP), distributed computing, vector databases, and MLOps practices such as CI/CD, containerization, and experiment tracking.
• A track record of turning research-stage AI ideas into scalable business applications.
Nashville, Tennessee
