Lead Data Scientist AI Engineer

We are hiring a Senior AI Engineer to join a newly formed AI Innovation Function, part of the Investment Banking AI & Technology team. This is not a back-office engineering role. This role follows the Forward Deployed Engineer philosophy: you will not build in isolation. You will be embedded with deal teams and industry/sector groups, understanding their day-to-day workflows and delivering tools that create immediate, measurable impact on how they originate, execute, and close deals.

You will work across the full stack—data pipelines, ML models, LLM-powered applications, and Salesforce integrations—with a bias for shipping fast, learning from real user feedback, and iterating relentlessly.

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.

QUALIFICATIONS

• 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.

PREFERRED QUALIFICATIONS

• 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.

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