Gen AI Full stack Developer
Location: Pan India
Experience: 7+ Years
Skills: AI , Python and React
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.
