Full Stack AI Engineer

Job Description: Full Stack AI Engineer / Agentic Application Engineer Future Hire | 2–3 Years Experience | Full-Time Role Summary: We are looking for a hands-on Full Stack AI Engineer who can independently build AI-enabled internal applications across frontend, backend, API integrations, and agentic workflows. The resource should be strong in Python/FastAPI and frontend development, with practical exposure to LLM APIs, RAG/agent workflows, and integration-heavy enterprise applications. Key Responsibilities: • Design and develop full-stack AI applications covering backend APIs, frontend screens, workflows, and integrations. • Build and maintain Python/FastAPI-based services for AI automation use cases. • Develop React/JavaScript-based frontend components and connect them cleanly with backend APIs. • Implement agentic workflows using tool-calling, orchestration, memory/state handling, and controlled execution patterns. • Build RAG or retrieval-based modules where required. • Integrate internal/external APIs, databases, and automation services securely and reliably. • Write modular, readable, maintainable code with testing discipline. • Coordinate with DevOps/platform teams for CI/CD and releases. • Document architecture, APIs, configuration, assumptions, and deployment requirements. • Work closely with stakeholders to convert requirements into solutions. Mandatory Skills / Experience: • 2–3 years hands-on software engineering experience. • Strong Python programming fundamentals. • Practical experience with FastAPI. • Frontend development using React and/or JavaScript/HTML/CSS. • Database fundamentals: SQL/PostgreSQL/MySQL and/or MongoDB. • Git/GitHub experience. • REST API design and debugging. • Understanding of LLM API integration and prompt/tool workflows. Preferred / Nice-to-Have Skills: • LangGraph, CrewAI, Semantic Kernel, LangChain. • MCP/tool-calling concepts and agent frameworks. • RAG implementation experience. • Docker basics. • AWS/GCP familiarity. • Enterprise integrations and automation tools. • Postman, pytest, API/frontend testing. • Banking/financial services exposure. KPIs / Success Measures • Deliver complete frontend-backend AI modules. • Build reliable FastAPI services. • Produce reusable components. • Maintain readable code and documentation. • Meet sprint commitments and timelines.