AI & Financial Engineering Developer

Job Description: AI & Financial Engineering Developer
Location: McLean, Remote
Assignment Type: Contract Only
Scheduled End: December 31, 2026
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.
Schedule: Standard

Interview Information:
Rounds: 1 round
Duration: 60 mins
Interview Type: MS Teams - Video Mandatory
Interview Placeholders: Targeting July 24 – 31st

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