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Position Overview
Our specialized financial services consulting firm has been engaged by an established financial technology and brokerage group to identify a small number of high-potential students for its AI Engineering Internship, joining the client’s internal AI transformation team.
This is not a coffee-and-slides internship. Selected students will work directly alongside our client’s senior engineering and department leads, contributing to production software modules, data workflows, and system integrations that the business actually runs on. The work is real, it is reviewed closely, and it ships.
The focus is practical engineering: connecting systems through APIs, structuring and cleaning data, and automating internal processes such as client onboarding and the client portal. The role suits students who want to see how a financial technology business operates from the inside and who learn fastest by building.
AI is at the core of how this team builds. Interns are expected to work with AI coding assistants and LLM tools every day: to design and write code, explore unfamiliar systems, generate tests, and automate workflows. Hands-on experience programming with AI is a requirement, not a nice-to-have.
Our client is looking for curiosity and tenacity above credentials. Students who perform well will be given increasing ownership over individual modules and a clear path toward a longer-term engagement with the firm.
Our Client
Our client is a financial technology group serving active and institutional traders, with an established product suite spanning execution, market data, and quantitative research infrastructure.
As the firm expands its institutional business, it is investing in the internal systems behind it: onboarding, client-facing portals, data pipelines, and the integrations that connect them. This search supports that build-out.
Location & Format
Location: New York, hybrid (3 days in office, Midtown, next building from Penn Station)
Format: Part-time or full-time options available
Reports to: Department leads
Key Responsibilities
· AI-assisted development – Use AI coding tools (e.g., Claude Code, CodeX, Cursor, GitHub Copilot) as a core part of daily work to design, write, test, and document code, and build LLM-powered steps into internal workflows where they add value
· System integration – Design, build, and maintain API connectors between internal and external databases and systems
· Module development & automation – Support the development and automation of core internal modules, including client onboarding and the client portal
· Data processing & management – Structure, clean, and analyze diverse datasets; set up reliable database connections and keep data logic consistent across systems
· Technical collaboration – Work directly with lead engineers and department heads to translate business logic into working code and automated scripts
· Independent problem-solving – Read documentation, investigate unfamiliar systems, and resolve technical problems independently before escalating
Target Candidate Profile
Fields of Study
· Master’s or upper-level Bachelor’s students in Quantitative Finance, Financial Engineering, Computer Science, Data Science, Physics, Computational Finance, Financial Mathematics,
· Ideally with a minor or concentration in Data Science or Finance
Track Record Beyond Coursework
Preference will be given to students who have shown initiative outside the standard curriculum, for example:
· Math, physics, or programming olympiads
· Coding competitions and hackathons
· Business case challenges
· Active roles in technical, quantitative, or finance student clubs
Requirements
· Analytical rigor – Strong mathematical foundation, logical reasoning, and a structured approach to problem-solving
· Software & data fundamentals – Practical programming experience with a solid grasp of software development principles, APIs, database architecture, and data structures
· Hands-on AI programming experience (required) – Demonstrated experience building software with AI coding assistants and LLM tools; be ready to walk through a concrete project and explain how you used AI to build it and how you verified its output
· Curiosity – Real curiosity about how systems and financial businesses work, evident in projects you started on your own
· Tenacity – The persistence to work through documentation and unfamiliar codebases and to finish difficult tasks without step-by-step guidance
· Coachability – Openness to direct code review and the ability to apply feedback quickly
· Clear written and spoken English
Strong Plus Qualifications
· Experience building with LLM APIs (OpenAI, Anthropic, or similar), prompt engineering, RAG, or agent frameworks
· Experience with REST APIs, webhooks, or integration and automation platforms
· Familiarity with relational databases and basic data modeling
· Personal or academic projects available on GitHub or a portfolio
· Interest in markets, trading, or financial technology
What This Role Is Not
To save everyone time: this is not a quantitative research or trading position. Our client is looking for strong data skills, sound logic, and solid software engineering fundamentals. Nor is it a role for someone who treats AI tools as optional: they are central to the day-to-day work. Equally, this is not a passive observation role. Students who prefer well-defined tasks with close supervision at every step are unlikely to enjoy it; those who like to take a vague problem, read up, and come back with working code will.
Application Note
If the LinkedIn application is no longer accepting submissions when you view this posting, please send your resume directly to ilona.vaziri@businessinvitee.net for consideration.
This search is being conducted by a specialized financial services consulting firm on behalf of an established financial technology and brokerage group. All applications will be treated in strict confidence. Only shortlisted candidates will be contacted for initial screening before being presented to our client.
Note: Candidates must be authorized to work in the United States.
Work arrangement
Yes
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