Johnson Electric Generative AI Engineer 

China, Guangdong Province, Jiangmen 

761214213

You will be joining a high-impact, hands-on CoE team that owns the full analytical stack: from edge data acquisition and cloud ingestion to model deployment and smart factory adoption.

You will own the end-to-end Gen AI technology stack for Johnson Electric. Design, build and govern reusable toolchains, unlock new use-cases and establish DevOps and MLOps best practices that can scale.

Architecture and Strategy

• Define Gen-AI architecture blueprints, design guidelines, and model-cards aligning with JE data privacy, OT-IT convergence and cost models.
• Establish enterprise patterns for RAG, model fine-tuning, agentic workflows, security isolation, and cost governance.

Platform Delivery

• Build and operate a reusable Gen-AI platform on Azure (AKS, AzureML, Azure OpenAI) provisioned via IaC (Terraform/Bicep) and managed through DevOps pipelines such as Azure DevOps.
• Integrate and orchestrate Gen-AI building blocks – commercial & open-source LLM APIs, MCP tooling, frameworks such as LangChain and LlamaIndex, vector databases, Azure AI Search indexes.

Productionize New Use Cases

• Partner with functional SMEs (quality, maintenance, logistics, R&D) to transform high-value ideas into production Gen-AI solutions.
• Guide teams through the full GenAI application lifecycle: problem framing, data acquisition, prompt and model design, human-in-the-loop validation, deployment, monitoring, and iterative improvement.
• Document reusable patterns and feed lessons learned back into the platform backlog to accelerate subsequent use-case onboarding.

• 5+ years in Data / AI engineering, 2+ years specifically building or productizing Gen-AI / LLM solutions in production.
• Deep understanding of LangChain / LlamaIndex (RAG, Agentic workflows).
• Expert in prompt engineering, agentic concepts, memory, and tool management
• Strong coding skills in Python (FastAPI, asyncio, Pydantic) and at least one typed language (Java, C#, or Go).
• Experience in Machine Learning preferably in manufacturing / IoT / edge AI.
• Expert in Azure Cloud Services for AI or equivalent.
• Hands-on with Docker/Kubernetes, GPU containers, CUDA drivers, and performance profiling.
• Proven experience translating business value metrics into technical architecture.
• Excellent stakeholder communication; able to navigate between OT, IT, cyber-security, and business teams.
• Fluency with DevOps & IaC (Azure DevOps, Terraform / Bicep, GitHub Actions).