AI & Financial Engineering Developer

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AI & Financial Engineering Developer

McLean, VA

July 14, 2026

Only considering candidates already local to the Washington, D.C. area who can start immediately.

We are seeking an experienced AI & Financial Engineering Developer with expertise in AI/ML, financial systems, and enterprise software development. This role is ideal for a hands-on engineer who can build AI-powered financial applications, develop scalable APIs, and collaborate with cross-functional teams to deliver innovative solutions.

Must-Have Qualifications

• 7 years of software development experience
• Strong experience building APIs and AI-powered applications
• Expert programming skills in Python with experience in Java, C , or Scala
• 1–3 years of experience in financial services or FinTech
• Experience developing applications using LLMs; experience with AI agents/agentic AI is highly preferred
• Experience with cloud platforms (AWS, Azure, or GCP) and modern software engineering practices
• Bachelor's degree preferred (Master's or PhD in a quantitative field is a plus)
  
  

Key Responsibilities

• Design, develop, and deploy AI/ML solutions for financial applications
• Build and optimize LLM, NLP, and generative AI applications
• Develop scalable APIs, microservices, and distributed systems
• Create data pipelines and implement MLOps best practices for model deployment and monitoring
• Develop quantitative models, financial analytics, and simulation tools
• Build real-time market data processing and financial analytics solutions
• Write production-quality, scalable, and maintainable code
• Collaborate with business stakeholders, quants, data engineers, and product teams
  
  

Required Technical Skills

• Languages: Python, Java, C , Scala, SQL
• AI/ML: PyTorch, TensorFlow, scikit-learn, Hugging Face, LangChain, LLMs
• Cloud & DevOps: AWS, Azure, or GCP; Docker, Kubernetes, CI/CD
• Data: Spark, Kafka, Airflow, PostgreSQL, MongoDB, Redis
• Experience with REST APIs, microservices, Git, and modern development workflows
  
  

Preferred Qualifications

• Experience with RAG architectures, LLM fine-tuning, and AI agents
• Knowledge of quantitative finance, derivatives, risk management, or portfolio analytics
• Experience with financial engineering libraries (QuantLib, pandas, NumPy)
• Familiarity with MLOps tools such as MLflow or Weights & Biases
• CFA, FRM, or other financial certifications are a plus
• Experience with reinforcement learning, real-time streaming, or low-latency systems is a plus
  
  

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