You will set the engineering standards for the team: architecture decisions, code quality, testing practices, and deployment patterns. You will work across the full stack-from data pipelines and ML models to LLM orchestration and Salesforce integrations-with the options to choose the right tool for each problem and the discipline to build for longevity., * 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.

• 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.
• Set engineering standards for the Innovation Team: code review practices, CI/CD pipelines, testing frameworks, and documentation norms that enable speed without sacrificing reliability.
• 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.
• Build and maintain data pipelines using Databricks and Dagster for feature engineering, model training, and analytics that feed AI capabilities.
• Perform rapid analysis and prototyping-translate a banker’s pain point into a working proof of concept within days, not weeks.
• Evaluate and integrate point solutions (Rogo.ai, Blueflame AI, Fellow.ai) via APIs, ensuring clean data flows and consistent user experiences within Salesforce.
• Implement security and data governance protocols appropriate for confidential deal information.

Artificial Intelligence (AI), Data Science, Embedded Systems, Investment Services, Artificial Intelligence (AI), Quality Assurance Methodology, Data Management, Salesforce.com, Customer Relationship Management (CRM), Banking Services, * 5+ years of software engineering experience with a strong full-stack foundation, including production experience building applications that serve demanding end users.

• Hands-on experience building applications or agents using large language models: prompt engineering, retrieval-augmented generation, multi-step orchestration, tool use, and evaluation frameworks.
• Experience deploying and operating multi-agent ecosystems in production - including reliability engineering, monitoring, failure recovery, and scaling agent infrastructure for enterprise workloads.
• Strong ML fundamentals-ability to train, evaluate, and deploy models, perform exploratory data analysis, and build feature pipelines.
• Rigorous engineering practices: you write tested, reviewed, well-documented code and build systems designed for maintainability, not just demos.
• Familiarity with capital markets, and preferably direct experience in or adjacent to investment banking, private equity, venture capital, or hedge funds.
• Experience with cloud infrastructure (Azure preferred), data platforms (Databricks/Spark), and orchestration tools (Dagster, Airflow, or equivalent).
• 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.

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