Job Description:
My client is seeking a seasoned AI engineer with a deep understanding of how Large Language Models (LLMs) work and can be integrated into a modern information-processing stack.
Responsibilities:
- Solve complex business tasks by harnessing state-of-the-art LLMs to extract, analyze, summarize, and operate with information in various document types.
- Utilize your excellent knowledge of the Gen AI tool stack to parse and chunk documents for specific application tasks, embeddings, retrieval augmented generation, and design complex information-processing flows with LLMs.
- Craft text instructions to reliably elicit desired task behavior from LLMs.
- Apply your deep expertise in Python, using idiomatic Pythonic techniques and standard libraries to build sophisticated systems quickly with minimal code.
- Leverage standard tools of the trade for machine learning engineers, such as WandB for ML flow, Prodigy, and open-source tools for data/document annotation.
- Design, run, and evaluate a series of experiments to enhance your team's understanding of how specific LLMs can be used to accomplish specific tasks.
Qualifications:
- Bachelor's degree in computer science or a related area, plus five years of experience, or a Master's degree with two years of experience.
- Significant, demonstrated experience in solving complex business tasks using LLMs.
- Excellent knowledge of Gen AI tool stack and standard machine learning tools.
- Deep expertise in Python and standard libraries.
- Experience in finance and investing is highly desirable.
About the Team:
My client is developing a Large Language Model (LLM)-first knowledge stack for investment professionals, including analysts and portfolio managers in equities, fixed income, high income, and direct lending. The stack will process various documents of interest to analysts, such as analyst reports, earnings notes, spreadsheet models, prospectuses, loan indentures, news reports, and regulatory filings.
The stack will support the deployment of personalized assistants that can assist principals in their full range of information consumption, processing, and production tasks. These tasks include summarizing documents from different points of view, answering questions about passages, extracting terms from documents, comparing extracted information across time and deals, consolidating and restating agreements, identifying trends, monitoring relevant changes, generating plausible scenarios, analyzing and decomposing factors, and generating reports. Assistants are personalized to their principal, deliver high-quality results, and are expected to improve over time with increased interaction.
