Senior Data Scientist – AI Engineer

William Blair•Chicago, IL

2d•$150,000 - $170,000•Hybrid

About The Position

Join William Blair, the Premier Global Partnership, as a Senior AI Engineer within the newly formed AI Innovation Function, part of the Investment Banking AI & Technology team. This role operates under the Forward Deployed Engineer philosophy, meaning you will be embedded with deal teams and industry/sector groups to understand their workflows and deliver tools that create immediate, measurable impact on how they originate, execute, and close deals. You will work across the full stack, including data pipelines, ML models, LLM-powered applications, and Salesforce integrations, with a focus on rapid deployment, user feedback, and iterative improvement.

Requirements

3+ years of software engineering experience with full-stack capability and a track record of shipping production applications.
Experience building applications or agents using large language models: prompt engineering, RAG architectures, LLM orchestration, or tool-use patterns.
Experience building interconnected multi-agent ecosystems — implementing agent coordination, shared tooling, and communication patterns across autonomous components.
Solid ML fundamentals: ability to perform data analysis, build and evaluate models, and work with feature pipelines.
Rigorous engineering habits—you believe in tested code, clean architecture, and building for maintainability from the start.
Familiarity with capital markets; experience in or adjacent to investment banking, private equity, venture capital, or hedge funds is strongly preferred.
Experience with cloud platforms (Azure preferred), data tools (Databricks, Spark), and pipeline orchestration (Dagster, Airflow, or similar).
Outcome-focused mindset—you care about whether bankers actually use what you build and whether it moves the needle on their productivity.

Nice To Haves

Experience in a Forward Deployed Engineer, solutions engineer, or embedded technical role with direct business stakeholder accountability.
Experience deploying multi-agent ecosystems into production environments — including operational monitoring, failure handling, and end-to-end lifecycle management.
Exposure to financial services workflows: deal execution, pitch preparation, due diligence, or financial modeling.
Experience working with Salesforce APIs or CRM platforms as integration surfaces.
A builder’s mentality: you have side projects, open-source contributions, or a portfolio that demonstrates curiosity and initiative beyond your day job.

Responsibilities

Build AI-powered features and agents using Enterprise Claude and proprietary ML models, integrated directly into the Salesforce workflows bankers use every day.
Develop LLM applications for banking use cases including automated comparable analysis, buyer recommendation, meeting intelligence summarization, and deal status briefings.
Design and maintain data pipelines using Databricks and Dagster for feature engineering, model training, and real-time analytics.
Work directly with deal teams and industry groups to identify high-impact automation opportunities and translate banker pain points into working solutions.
Perform rapid prototyping and exploratory analysis—build proof-of-concept tools quickly to validate ideas before investing in production-grade implementations.
Integrate third-party AI tools (Rogo.ai, Blueflame AI, Fellow.ai) via APIs and ensure seamless data flows across the composable architecture.
Write well-tested, production-quality code with rigorous engineering practices: code reviews, CI/CD, monitoring, and documentation.
Contribute to the team’s engineering standards and share knowledge as the team scales.