Remote- AI & Financial Engineering Developer

AI & Financial Engineering Developer

Location: McLean, Remote

Call notes:

This is a remote opportunity.

We use a variety of quantitative models to forecast mortgage defaults and prepayments in order to assess financial risk.

The goal is to leverage AI to assist users throughout the model execution lifecycle, including formatting inputs, interpreting data elements, and providing guidance during model execution.

Since we have different models for different mortgage products, the AI should be able to understand the specific model being executed and provide contextual assistance accordingly.

The AI should be capable of analyzing the underlying model code and business logic to explain what is happening during execution, identify potential issues, and help diagnose model outputs.

This role requires a unique combination of AI expertise and Financial Engineering knowledge, as the individual will be working at the intersection of both domains.

Development will primarily be done in Python.

Candidates should have experience with quantitative financial models, including prepayment models, credit risk models, valuation models, and risk models.

Similar to industry-standard models (e.g., Opus), all models go through required security and governance checks before being deployed. They are then hosted securely within internal endpoints for enterprise use.





Job Description: AI & Financial Engineering Developer

Location: McLean, Remote

Must Have Qualifications: 7+ years of software development experience, including experience with API development, AI application development, and programming languages such as Python, C++, and Scala. Candidates should have 1-3 years of financial industry experience, with exposure to large language models (LLMs) and agentic AI development is a strong plus. A degree is preferred but not required. Prior experience with Fannie or Freddie is a strong plus.







Position Overview

We are seeking a highly skilled AI & Financial Engineering Developer who combines deep expertise in artificial intelligence/machine learning with quantitative finance and financial engineering. This hybrid role is ideal for a technologist who thrives at the intersection of cutting-edge AI and complex financial systems.



Key Responsibilities

AI & Machine Learning

Design, develop, and deploy machine learning models and AI-powered applications for financial use cases

Build and optimize deep learning, NLP, and generative AI solutions

Develop data pipelines and feature engineering frameworks for model training and inference

Implement MLOps best practices including model versioning, monitoring, and continuous deployment

Stay current with state-of-the-art AI research and evaluate applicability to financial domains



Financial Engineering

Develop quantitative models for pricing, risk management, and portfolio optimization

Implement algorithmic trading strategies and backtesting frameworks

Build financial simulation engines (Monte Carlo, stochastic modeling, etc.)

Design and develop derivatives pricing models and fixed-income analytics

Create real-time market data processing and analytics systems



Software Development

Write production-quality, scalable, and maintainable code

Architect and build high-performance distributed systems

Develop RESTful APIs and microservices for financial applications

Implement robust testing, CI/CD pipelines, and documentation practices

Collaborate with cross-functional teams including traders, quants, risk managers, and data engineers



Required Qualifications

Education: Master s or PhD in Computer Science, Financial Engineering, Quantitative Finance, Mathematics, Physics, or a related quantitative field

Experience: 7+ years of professional software development experience, with at least 3 years in AI/ML and 2+ years in financial services or fintech

Programming Languages: Expert proficiency in Python; strong skills in C++, Java, or Scala

AI/ML Expertise: Hands-on experience with TensorFlow, PyTorch, scikit-learn, and large language models (LLMs)

Financial Knowledge: Strong understanding of financial instruments (equities, fixed income, derivatives, structured products), market microstructure, and quantitative risk measures (VaR, Greeks, CVA)

Mathematics: Advanced knowledge of stochastic calculus, linear algebra, probability theory, and numerical methods

Data & Infrastructure: Experience with SQL/NoSQL databases, cloud platforms (AWS, Azure, or Google Cloud Platform), and big data technologies (Spark, Kafka)



Preferred Qualifications

CFA, FRM, or equivalent financial certification

Experience with reinforcement learning applied to trading or portfolio management

Knowledge of blockchain/DeFi protocols and smart contract development

Familiarity with regulatory frameworks (Basel III/IV, MiFID II, Dodd-Frank)

Publications in AI/ML or quantitative finance journals

Experience with real-time streaming systems and low-latency architectures

Proficiency with LLM fine-tuning, RAG architectures, and AI agents for financial applications



Technical Stack (Preferred Experience)

Category Technologies

Languages Python, C++, Java, SQL, R

AI/ML PyTorch, TensorFlow, Hugging Face, LangChain, scikit-learn

Finance Libraries QuantLib, Zipline, Backtrader, pandas, NumPy

Cloud & Infra AWS/Azure/Google Cloud Platform, Docker, Kubernetes, Terraform

Data Spark, Kafka, Airflow, PostgreSQL, MongoDB, Redis

DevOps Git, CI/CD, MLflow, Weights & Biases