Investment Banking Associate - AI Product & Engineering

Cantor Fitzgerald•New York, NY

About The Position

Cantor Fitzgerald is creating a dedicated AI function within its Investment Banking division. Reporting to the Head of AI, you will design, prototype, and launch AI‑driven applications that streamline pitch preparation, comparable analysis, diligence summarization, and market surveillance. This role blends deep investment‑banking knowledge with hands‑on AI engineering to deliver production‑grade tools that enhance banker productivity and client outcomes.

Requirements

2+ years of experience in investment banking, corporate finance, equity research, or a closely related financial‑services role.
Hands‑on experience building and deploying LLM‑based applications beyond tutorials, including context pipelines and evaluation frameworks.
Full‑stack prototyping skills: data integration, back‑end logic, and user‑interface development; proficient in at least one programming language (Python, JavaScript, etc.).
Familiarity with financial data APIs and platforms such as Bloomberg, PitchBook, FactSet, and SEC EDGAR.
Strong understanding of IB deal processes: pitch construction, financial modeling, CIM/OM preparation, and capital‑markets execution.
Proven ability to work autonomously, define problems, and drive solutions from concept to production.
Excellent communication skills; able to translate technical concepts for senior bankers and business context for engineers.
Experience navigating compliance, data‑privacy, and regulatory requirements in a financial‑services environment.
Bachelor’s degree in finance, economics, computer science, engineering, or a quantitative discipline; advanced degree a plus.
Ownership mindset and judgment in ambiguous, fast‑paced environments.

Nice To Haves

advanced degree a plus

Responsibilities

Design and build AI‑powered tools for core IB workflows such as pitch decks, CIM drafting, comparable company analysis, and diligence summarization.
Own the end‑to‑end development cycle: data sourcing, LLM integration, back‑end logic, and user‑facing interfaces.
Integrate solutions with firm data platforms (FactSet, PitchBook, SEC EDGAR, internal databases) to surface relevant information in real time.
Maintain production quality by monitoring performance, handling issues, and iterating based on banker feedback.
Partner with bankers, analysts, and associates to translate high‑friction tasks into tractable AI problems.
Develop documentation, training materials, and support channels to drive user adoption and enablement.
Manage the AI request intake pipeline, prioritize projects, and communicate timelines to stakeholders.
Track adoption metrics, time‑savings, and business impact; use data to continuously improve the AI program.
Navigate compliance, data‑sensitivity, and regulatory constraints in collaboration with InfoSec and Legal teams.
Stay current with LLM advancements and evaluate cost/quality trade‑offs for production deployment.