Job Description & Details
This is a hands-on engineering contract role focused on building quantitative finance systems, risk analytics, and cutting-edge AI applications in McLean, VA. You will be sitting at the intersection of modern web development, financial modeling, and emerging agentic AI tools. If you enjoy building data-heavy web apps and want to touch LLM workflows in a financial context, this gig offers some genuinely interesting architectural challenges.
What You'll Actually Be Doing
Your day-to-day will involve designing, developing, and supporting systems tailored for financial research, analytics, and market/credit risk monitoring. You'll spend a lot of time writing and reviewing Python code, crafting efficient back-end logic with Flask or Dash, and wiring up robust front-end interfaces using React and modern JavaScript. Beyond feature development, you'll also collaborate with financial research teams to translate complex quantitative requirements into reliable, production-ready software solutions.
The Core Tech Stack
Python is the absolute heartbeat of this stack, specifically paired with data processing powerhouses like Pandas and NumPy, alongside web frameworks like Flask and Dash. On the client side, you need to be comfortable bringing designs to life with React, JavaScript, HTML, CSS, and tools like Tailwind or jQuery. Because this is heavily data-driven financial tech, a solid command of SQL, relational databases, and a Linux/Unix environment is non-negotiable. If you've dabbled in building agentic AI applications with LLMs or cloud platforms like AWS and Google Kubeflow, you'll immediately stand out from the applicant pool.
Interview Expectations
Expect a rigorous 60-minute in-person technical discussion where the interviewer will likely ask you to optimize a slow Pandas data pipeline or explain how you would design a scalable web dashboard feeding off massive numerical datasets. The hiring manager is secretly testing whether you can write clean, performant code under constraints while actually understanding the financial math or data structures you're manipulating, rather than just blindly copying syntax from Stack Overflow.
Application Advice
To get past the ATS, your resume needs to prominently feature exact keyword matches like Python, Flask, Dash, Pandas, NumPy, React, and JavaScript. Don't just list them as bullet points; weave them into your project descriptions, explicitly highlighting any past work involving data-heavy web applications, quantitative analytics, or financial systems. If you have experience with AI agents, LLM integrations, or cloud deployments, put those right at the top.