
AI/ML Engineer
Role summary
We are seeking an AI/ML Engineer to build and optimize RAG pipelines, design agentic AI systems, and develop applied AI solutions. This role involves integrating AI services on Kubernetes and cloud environments, and working directly with customers to deliver technical implementations. The ideal candidate will have strong experience with LLMs, frameworks like LangChain/LlamaIndex, Python, vector databases, and Kubernetes. Experience with PLM, prompt engineering, and enterprise architecture is a plus.
Responsibilities:
Build and optimize RAG pipelines, vector databases, embeddings, and document-processing workflows.
Design agentic AI systems (tool calling, orchestration, reasoning loops, workflow automation).
Develop Applied AI solutions that integrate with customer backend systems, APIs, and data sources.
Implement AI services on Kubernetes, cloud environments, or customer-controlled infrastructure.
(Good to have) Integrate AI workflows with PLM systems or enterprise knowledge platforms.
Work directly with customers to deliver high-quality technical implementations, demos, and documentation.
Required Skills:
Strong experience with LLMs, LangChain / LlamaIndex / custom agent frameworks.
Expertise in Python, vector DBs (Elastic, Milvus, Pinecone, etc.), embeddings, chunking.
Experience with Kubernetes, Docker, and scalable API deployment.
Ability to understand customer workflows and translate them into technical solutions.
Nice to Have:
Knowledge of PLM, PDM, or enterprise engineering workflows.
Experience with RAG evaluation, prompt engineering, and grounding strategies.
Familiarity with enterprise architecture, IAM, SSO, or security constraints
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