SAP BTP Application Development

Fittbot; Hyderabad, Pune District, Bengaluru; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Fittbot in Hyderabad is seeking a skilled engineer to deliver end-to-end SAP BTP solutions. You will engineer frontend and backend components, integrate SAPUI5 UIs with robust APIs, and model data for SAP HANA-backed persistence. You will also configure deployment artifacts and drive AI-native workflow features.

The role emphasizes clean code, testing discipline, and CI/CD readiness within an Agile/Scrum team, delivering production-ready apps on SAP BTP.

Qualifications

• Primary skills focused on AI-native SAP BTP development across platform architecture, admin basics and frontend/backend build.
• Hands-on full-stack implementation covering backend services, APIs, data models, and UI integration.
• Strong software engineering fundamentals: clean code, testing mindset, CI/CD, production-grade readiness.

Responsibilities

• Architect, build, deploy and manage SAP BTP applications.
• Develop backend services and APIs (CRUD, business logic, validations).
• Create responsive UI with SAPUI5 and integrate with APIs.
• Model data and connect to SAP HANA persistence patterns.
• Set up subaccounts, destinations, and deployment workflows (MTA).
• Implement AI-native patterns: tool calls, memory/state handling, RAG experiences, monitored quality.

Skills

SAP BTP basics

Full-stack dev

Node.js APIs

SAPUI5 UI

Data modeling

CI/CD

Agile/Scrum

Education

Bachelor's degree in CS/Engineering

Tools

SAP HANA

Git

Job description

) Cloud Application Engineering on SAP BTP

• Build, deploy, and manage applications in a cloud-based environment on SAP BTP, covering platform fundamentals, administration touchpoints, solution design, and both frontend and backend development.

2) Full Stack Delivery (Frontend + Backend) with SAP BTP Patterns

• Develop backend services and APIs (including CRUD + business logic + validations) using Node.js-based service patterns, and expose services for frontend consumption.
• Build responsive UI components using SAPUI5 (XML views, controllers, MVC approach) and integrate with backend APIs for data display and write-back flows.

3) Data Modeling & Persistence Foundations

• Design database artifacts and data models (tables/views/entities/relationships) and connect applications to SAP HANA-based persistence patterns used in BTP app development scenarios.

4) Platform Setup & Deployment Engineering

• Configure subaccounts/service instances/roles and set up development spaces configure connectivity artifacts (e.g., destinations) for integrating external APIs and databases as part of application delivery.
• Package and deploy applications using multi-target application (MTA) style deployment artifacts where applicable.

5) AI Native Application Patterns (Agentic + LLM Integrated) (Build expectation)

• Implement LLM-integrated features as part of BTP applications (e.g., assistant in the workflow ), including: tool calling, orchestration, memory/state handling, and safe action boundaries.
• Build retrieval-grounded experiences (RAG-style) over enterprise content and APIs where the solution requires grounded responses, with clear fallback behaviors.
• Create evaluation loops (offline test sets + online monitoring) so AI behavior changes are measurable and regressions are caught early.

6) Engineering Excellence (Quality, DevOps, Collaboration)

• Use Git-based workflows for source control and collaboration, and operate in Agile/Scrum delivery models.
• Debug and resolve backend/database/service exposure issues encountered during full-stack delivery.

Primary Skills (AI Native Must Have)

• SAP BTP application development fundamentals spanning platform architecture, administration basics, solution design, and frontend/backend build.
• Hands-on full-stack implementation patterns evidenced by: backend services + APIs + data models + UI integration.
• Strong software engineering fundamentals (clean code, testing mindset, CI/CD orientation) and ability to move from prototype to production (AI native expectation).