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The Full Stack Engineer builds end-to-end, AI-enabled research applications that deliver data, research platforms and insights seamlessly to investors and researchers. Embedded within DS&S research pods - asset-class focused sub-teams - this role requires collaboration with AI Product leads, data leads and the investor teams to transform research workflows into intuitive, interactive and resilient production-grade applications.
Job Responsibility:
Design, build and deploy investor research apps end‑to‑end owning frontend, backend APIs, data/AI features, and intuitive UIs that fit investor workflows
Partner with the Research Pod Leads and investors to translate business requirements into product design and iteratively build high quality web applications
Write reusable, maintainable, and extensible code, and create documentation for team members
Build modules, libraries and frameworks that can help scale building web applications based on similar patterns
Ensure that the web applications have the required instrumentation and observability that helps investors build confidence in operational health of the app
Act as a core pod member and coordinate with horizontal engineering teams to leverage shared tooling and contribute to building tools and development processes across pods
Requirements:
B.S. / M.S. college degree in Computer Science, Engineering, or related subject area
3+ years hands-on software development exposure in full-stack applications
Experience building production-grade full-stack applications with a modern Python backend (FastAPI, Flask, etc.) and TypeScript/JavaScript frontend (React/Next.js.)
Strong problem-solving, analytical, and software architecture skills
Experience deploying and operating full-stack systems on Cloud Platforms (GCP/Azure) including CI/CD, observability, and analytics
Strong capability to translate business workflows into scalable technical designs and intuitive applications
Excellent communication skills, with the ability to gather requirements from investors and product owners, collaborate and deliver iteratively, and produce clear documentation for non-technical partners
Nice to have:
Proven ability to work closely with data engineering teams on high-volume data pipelines and data visualization using modern cloud platforms such as BigQuery or Snowflake
Experience building applications with leading AI model providers such as Open AI, Vertex, and/or Anthropic
Hands-on experience with analytics and AI/LLM features (prompt engineering, RAG/retrieval, agentic orchestration, LLM monitoring, multi-modality inputs)
Experience building research, analytics, or portfolio/risk/market-data applications within investment management or broader financial services