Lead Data Scientist - AI Engineer
Actively Hiring
Full-time $186k - $222k Posted 9 days ago
Responsibilities
- check_circle Design and build the integration layer between Enterprise Claude, Salesforce CRM, and our proprietary ML models, creating the orchestration backbone for AI-powered banking workflows.
- check_circle Develop AI agents and multi-step LLM applications for high-value use cases: CIP first-draft generation, buyer landscape analysis, intelligent process letter drafting, and deal status automation.
- check_circle Set engineering standards for the Innovation Team: code review practices, CI/CD pipelines, testing frameworks, and documentation norms that enable speed without sacrificing reliability.
- check_circle Work directly with deal teams and industry/sector groups to understand workflows, identify automation opportunities, and iterate on deployed tools based on real-world banker feedback.
- check_circle Build and maintain data pipelines using Databricks and Dagster for feature engineering, model training, and analytics that feed AI capabilities.
- check_circle Perform rapid analysis and prototyping—translate a banker's pain point into a working proof of concept within days, not weeks.
- check_circle Evaluate and integrate point solutions (Rogo.ai, Blueflame AI, Fellow.ai) via APIs, ensuring clean data flows and consistent user experiences within Salesforce.
- check_circle Implement security and data governance protocols appropriate for confidential deal information.
- check_circle 5+ years of software engineering experience with a strong full-stack foundation, including production experience building applications that serve demanding end users.
- check_circle Hands-on experience building applications or agents using large language models: prompt engineering, retrieval-augmented generation, multi-step orchestration, tool use, and evaluation frameworks.
- check_circle Experience deploying and operating multi-agent ecosystems in production — including reliability engineering, monitoring, failure recovery, and scaling agent infrastructure for enterprise workloads.
- check_circle Strong ML fundamentals—ability to train, evaluate, and deploy models, perform exploratory data analysis, and build feature pipelines.
- check_circle Rigorous engineering practices: you write tested, reviewed, well-documented code and build systems designed for maintainability, not just demos.
- check_circle Familiarity with capital markets, and preferably direct experience in or adjacent to investment banking, private equity, venture capital, or hedge funds.
- check_circle Experience with cloud infrastructure (Azure preferred), data platforms (Databricks/Spark), and orchestration tools (Dagster, Airflow, or equivalent).
- check_circle Outcome orientation—you measure success by business impact delivered, not features shipped.
Preferred qualifications
- Experience in a Forward Deployed Engineer, solutions engineer, or embedded technical role where you owned outcomes alongside business stakeholders.
- Prior work with Salesforce APIs, SOQL, or CRM integration patterns.
- Experience architecting production-grade, interconnected multi-agent ecosystems — designing agent coordination patterns, shared tooling layers, and communication protocols across autonomous components.
- Experience building AI tools for financial professionals, including document generation, financial analysis automation, or deal workflow tooling.
- Contributions to engineering culture: mentoring, establishing best practices, or leading technical design reviews in a small-team environment.
About the company
- check_circle California Consumer Privacy Act Privacy Notice (CCPA)
- check_circle General Data Protection Regulation Privacy Notice (GDPR)
Tags & Focus Areas
Ai Ai Engineer Machine Learning Data Science Generative Ai
About William Blair & Company
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