Senior Remote Quantitative Developer

McLean, VA July 20, 2026 We are seeking an experienced remote Senior Quantitative 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