Full-Stack Engineer

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

• Frontend Development: React, TypeScript, state management, component libraries, responsive design

• 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