Role: AIML Engineer
Location: Santa Clara Valley CA
Work Model: Hybrid (3 days a week)
Job Description:
Background: Looking for an experienced AI Engineer to design build and deploy enterprise-grade AI solutions for customers. The ideal candidate should have hands-on expertise with Retrieval-Augmented Generation (RAG) Agentic AI workflows and LLM-based automation with the ability to integrate AI systems into complex enterprise environments.
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
Role: AIML Engineer Location: Santa Clara Valley CA Work Model: Hybrid (3 days a week) Job Description: Background: Looking for an experienced AI Engineer to design build and deploy enterprise-grade AI solutions for customers. The ideal candidate should have hands-on expertise with Retrieval-Augm...
Role: AIML Engineer
Location: Santa Clara Valley CA
Work Model: Hybrid (3 days a week)
Job Description:
Background: Looking for an experienced AI Engineer to design build and deploy enterprise-grade AI solutions for customers. The ideal candidate should have hands-on expertise with Retrieval-Augmented Generation (RAG) Agentic AI workflows and LLM-based automation with the ability to integrate AI systems into complex enterprise environments.
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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