Senior Full Stack - Developer

Key Responsibilities

Application Development Design, develop, and maintain full stack web applications that integrate AI/ML models, knowledge graphs, and search interfaces for financial research. Build modern, responsive UIs using React.js (or equivalent) with a focus on usability and performance. Develop scalable, secure backend services using Python (FastAPI/Flask/Django), Node.js, or similar, including REST and GraphQL APIs. Integrate front-end components with AI/LLM services (e.g., GPT, Claude), document processing APIs, and financial datasets. Collaborate with DevOps and Cloud teams to deploy applications using CI/CD pipelines, containers (Docker), and orchestration (Kubernetes). Ensure production-grade observability, logging, and monitoring of full stack components. Work alongside data scientists and ML engineers to embed AI capabilities into end-user applications. Collaborate with UX teams to translate user feedback from investment professionals into intuitive features. Contribute to code reviews, design discussions, and agile ceremonies. Follow best practices in secure coding, data access control, and compliance (especially for financial systems). Ensure all code aligns with Carlyle's enterprise architecture, data governance, and cybersecurity policies.

Required Qualifications :

5+ years of professional experience as a full stack or software engineer building enterprise-grade web applications. Expertise in: Frontend: React.js, TypeScript, Redux, Tailwind/CSS-in-JS Backend: Python (FastAPI, Flask, Django), Node.js, or similar Database: PostgreSQL, MongoDB, or graph/semantic databases (e.g., Neo4j, Amazon Neptune) Cloud: AWS preferred (Lambda, ECS, S3, API Gateway, SageMaker a plus) Version Control & CI/CD: Git, Jenkins/GitHub Actions Strong understanding of integrating AI/ML APIs and handling large volumes of financial and textual data. Familiarity with RESTful APIs, GraphQL, and microservices-based architecture. Experience working in financial services, private equity, or asset management technology teams. Exposure to AI/ML, NLP, or LLM-based applications. Familiarity with vector databases, document search tools (e.g., Elasticsearch), and RAG (retrieval-augmented generation) workflows. Experience working in an Agile or Scrum environment.