Remote- AI &amp Financial Engineering Developer

AI& Financial EngineeringDeveloper

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 AIto 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 AIshould be able to understand the specific model being executed and provide contextual assistance accordingly.
The AIshould 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 AIexpertise 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 AIapplication 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 AIdevelopment 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 Developerwho 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 AIand 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 AIsolutions
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 AIresearch 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: Masters 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 GCP) 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 AIagents 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/GCP Docker Kubernetes Terraform
Data Spark Kafka Airflow PostgreSQL MongoDB Redis
DevOps Git CI/CD MLflow Weights & Biases

Required Skills:

Python