Overview You will design, prototype, and launch AI-driven tools that streamline core IB workflows, enabling faster pitch prep, comps analysis, and diligence summaries. You'll work within the Investment Banking division reporting to the Head of AI, blending investment-banking knowledge with hands-on AI engineering to deliver production-grade solutions. You'll surface real-time data from firm platforms to support bankers and drive client outcomes. You'll own end-to-end development, from data sourcing to user interfaces, and continually improve solutions with banker feedback.
Compensation / Benefits Responsibilities- Design and build AI-powered tools for core IB workflows (pitch decks, CIM drafting, comparables, diligence summaries)
- Own end-to-end development: data sourcing, LLM integration, back-end logic, user interfaces
- Integrate with firm data platforms (FactSet, PitchBook, SEC EDGAR, internal databases) for real-time surfacing
- Monitor production quality, resolve issues, and iterate based on banker feedback
- Collaborate with bankers, analysts, and associates to translate tasks into AI problems
- Develop documentation, training materials, and support channels to drive adoption
- Manage AI request intake, prioritize projects, and communicate timelines
- Track adoption metrics and business impact to continuously improve AI program
- Navigate compliance, data-sensitivity, and regulatory constraints with InfoSec and Legal teams
- Stay current with LLM advancements and cost/quality trade-offs for production deployment
- Maintain production readiness through performance monitoring and issue handling
Key requirements- 2+ years in investment banking, corporate finance, equity research, or related financial-services role
- Hands-on experience building and deploying LLM-based applications beyond tutorials
- Full-stack prototyping skills (data integration, back-end logic, UI); proficient in Python, JavaScript, or similar
- Familiarity with Bloomberg, PitchBook, FactSet, and SEC EDGAR
- Strong understanding of IB deal processes: pitch, CIM/OM, capital-markets execution
- Autonomous problem solver who can drive from concept to production
- Excellent communication to translate tech concepts for bankers and business context for engineers
- Experience navigating compliance, data privacy, and regulatory requirements in financial services
- Bachelor's degree in finance, economics, computer science, engineering, or quantitative discipline; advanced degree a plus
- Ownership mindset in fast-paced, ambiguous environments
- Strong communication
- Autonomy and initiative
- Cross-functional collaboration
- LLM integration
- Data integration
- Back-end development
