Full-Stack Engineer

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Capital Fund Management (CFM) In collaboration with the Research teams, the Front Prediction team develops and maintains the prediction models (alpha signals) used to make decisions for our automated trading systems. As a member of this team, you will play a key role in the full lifecycle of predictive models, from research integration to robust, scalable production deployment. We seek a Full Stack Engineer with 5-10 years of experience to build and scale our Prediction Services platform. You'll develop cloud-native infrastructure, AI-powered applications, and user interfaces that enable quantitative researchers to deploy and monitor predictive models across global markets. Banking or hedge fund experience highly valued. Key Responsibilities

Design and implement scalable APIs and backend services for predictor deployment, testing and orchestration Build React-based frontends for model monitoring, metadata management, and what-if analysis capabilities Develop and integrate generative AI agents for code generation, workflow automation, and model transformation Architect cloud infrastructure (AWS preferred) with IaC practices and comprehensive observability Provide L2 support for production systems serving quantitative trading strategies Required Technical Skills

Cloud Infrastructure: Production experience with AWS, GCP, or Azure (compute, storage, networking, security) Backend Development: Python API development (FastAPI/Flask), service architecture (REST over HTTP), async patterns AI & Agents: Hands-on experience with LLMs, agent frameworks (LangChain/LangGraph), prompt engineering Databases: SQL proficiency with Oracle or PostgreSQL, query optimization, schema design CI/CD: Terraform, Jenkins/GitLab CI, containerization (Docker & Kubernetes), automated testing Observability: Metrics, logging, tracing, alerting (Prometheus/Grafana/ELK or cloud-native equivalents) Preferred Technical Skills

AWS Ecosystem: CodeBuild, S3, ECR, ECS/EKS, Lambda, CloudWatch, IAM best practices Distributed Computing: Ray framework for ML workload orchestration and parallel processing Vector Databases: ChromaDB, Pinecone, or Weaviate for RAG applications ML Frameworks: scikit-learn, PyTorch for model integration and inference pipelines Soft Skills & Team Dynamics

Collaborate within cross-functional Agile/Scrum teams (researchers, software engineers, quant devs) Strong communication skills for technical documentation and stakeholder engagement Problem-solving mindset with ability to triage production issues and provide L2 support Adaptability to evolving requirements in a fast-paced quantitative finance environment Seniority level

Mid-Senior level Employment type

Full-time Job function

Information Technology Industries

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Paris, Île-de-France, France .

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