Senior AI Agent Developer (CAP, Python, LangGraph, SAP AI Core)

Description:

Project Description
Independent work on the development and enhancement of AI-driven business solutions on SAP BTP by designing and implementing agent-based workflows, backend services, and integrations. The project focuses on extending enterprise AI capabilities through scalable, production-ready solutions that interact with SAP systems and business data.

Tasks
The external contractor will independently:
Design and implement AI agent workflows using LangGraph and Python.
Develop and enhance CAP-based backend services, including CDS models and OData APIs.
Configure and integrate SAP AI Core and Generative AI Hub capabilities within solution architectures.
Build and maintain MCP-compatible tool interfaces and agent integrations.
Develop data persistence and retrieval mechanisms using SAP HANA Cloud.
Implement AI workflow orchestration, intent-routing logic, and human-confirmation mechanisms.
Create technical documentation including architecture decisions, solution designs, and interface specifications.
Develop automated tests for agent workflows, backend services, and integrations.
Configure observability, monitoring, experiment tracking, and performance measurement capabilities where required.
Assist in integration of AI capabilities with SAP UI5/Fiori-based applications.
Deliver technical recommendations regarding AI workflow implementation, optimization, and scalability.

Deliverables
Implemented LangGraph-based agent workflows according to approved solution specifications.
CAP service enhancements, including CDS artifacts and OData service definitions.
SAP AI Core integration components and configuration artifacts.
MCP-compatible tool interfaces and integration endpoints.
SAP HANA Cloud data models and persistence components supporting delivered functionality.
Technical design documentation and architecture decision records (ADRs).
Automated test suites and execution results.
Deployment-ready source code committed to the client's approved repository.
Monitoring and observability configuration documentation where applicable.
Knowledge transfer documentation covering delivered solution components.

Acceptance Criteria (How SAP Verifies Completion)
SAP verifies completion through review of delivered artifacts against approved requirements and backlog items.
Acceptance includes:
Delivered functionality successfully performs the agreed business process or use case.
Solution components comply with defined architecture and integration requirements.
Code successfully passes agreed quality checks and automated tests.
APIs and integrations function according to documented specifications.
Technical documentation is complete and reflects delivered functionality.
Deliverables are demonstrated and accepted during sprint reviews, solution walkthroughs, or designated acceptance sessions.
Any identified defects classified as critical or high severity are resolved prior to acceptance.

SLAs / KPIs (Completion Target / Timing)
Deliverables completed within agreed sprint or project milestones.
Documentation and code artifacts delivered together with functional components.
Defects identified during acceptance testing addressed within agreed delivery timelines.
Development activities completed according to agreed backlog priorities and release schedules.
Solution deliverables meet agreed quality standards and testing requirements prior to handover.

Candidate Profile
Required Experience
Strong experience developing AI-based applications using Python.
Experience with LangGraph or comparable agent orchestration frameworks.
Proven experience with SAP Business Technology Platform (SAP BTP).
Experience developing SAP CAP applications using Node.js.
Strong knowledge of SAP HANA Cloud data modelling and persistence concepts.
Experience designing REST/OData-based service integrations.
Experience integrating Large Language Models (LLMs) into enterprise applications.
Experience implementing automated testing and code quality controls.
Ability to create technical architecture and solution documentation.
Proficiency in English.

Preferred Experience
SAP AI Core and Generative AI Hub.
MCP (Model Context Protocol) implementations.
AI observability and experiment-tracking solutions (e.g., MLflow).
OpenTelemetry-based monitoring and tracing.
SAP UI5 and Fiori application development.
Vector databases, RAG architectures, and semantic search solutions.
SAP API Management.
Enterprise AI governance and Responsible AI practices.

No

Known Resource Email Address

Additional Details

• Risk Level : Level 4
• Known Resource Email Address : (No Value)
• Known Resource First Name : (No Value)
• Known Resource Last Name : (No Value)
• Known Resource Phone Number : (No Value)
• Work delivery location (Country) : Germany | DE
• Work delivery location (City) : remote
• User Type : 33 Nonbillable Self Employed Worker