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
