AIBCI

1. Build Production-Grade Agentic AI Systems
-Design and implement AI agent architectures for decision support automation and continuous improvement use cases.
-Develop single-agent and multi-agent workflows including task decomposition tool usage and agent coordination.
-Ensure agent behavior is controllable observable and explainable in production environments.

2. Knowledge-Driven AI & Reasoning
-Integrate Knowledge Graphs to encode domain concepts relationships constraints and best practices.
-Enable knowledge-based reasoning to complement LLM-based inference (hybrid AI).
-Work with domain experts to translate implicit business knowledge into structured reusable models.

3. AI Platform & System Engineering
-Contribute to internal platforms such as Agent Builder Agent Hub and AI-enabled workflow systems.
-Design modular reusable components for agents tools workflows and APIs.
-Ensure solutions meet enterprise standards for reliability security scalability and maintainability.

4. Engineering Excellence & Lifecycle Ownership
-Take ownership of features end-to-end: design implementation testing deployment and operation.
-Apply software engineering best practices: versioning testing logging tracing and performance monitoring.
-Support long-term evolution from PoC pilot multi-site rollout.

5. Collaboration & Enablement
-Collaborate closely with product managers domain experts and other engineers across regions.
-Support internal enablement by sharing best practices in AI engineering agent design and knowledge modeling.
-Mentor junior engineers and help shape team-level engineering standards.

Qualifications :

1. Core Technical Competencies
-Solid experience in backend software development (e.g. C# .NET Java Python).
-Strong software engineering fundamentals: modular design APIs testing maintainability.
-Proven experience delivering production systems not only PoCs or demos.
-Experience integrating AI/LLM capabilities into applications in a controlled reliable way.

2. AI & Agent-Oriented Engineering
-Understanding of AI system engineering beyond model usage (context management versioning failure handling observability).
-Experience or strong interest in AI agents workflows or automation systems.
-Ability to combine deterministic logic and AI reasoning to build explainable solutions.
-Familiarity with knowledge-driven approaches (e.g. Knowledge Graphs semantic modeling RAG) is a strong plus.

3. System & Integration Skills
-Experience integrating with enterprise systems and APIs (e.g. MES WMS ERP data platforms).
-Understanding of scalable service-oriented or event-driven architectures is preferred.

4. Professional Attributes
-Strong sense of ownership and quality with end-to-end responsibility.
-Curious adaptable and motivated to grow with Agentic AI and intelligent systems.
-Able to work effectively in cross-functional and international teams.
-Communicates clearly and collaborates constructively.

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Remote Work :

No

Employment Type :

Full-time