Role - Sr Gen AI Lead
Location - San Jose CA (Onsite)
Number of opening :-10
GenAI Engineers build the core intelligence layer-agents workflows prompts and decision logic-that power enterprise AI applications.
60% on Python 20% Agent & RAG Frameworks Agentic AI Python Coding Test- F2F interview (Santa Clara CA )
Technology Stack (Priority Order)
1. Languages: Python
2. Agent & RAG Frameworks: LangChain LlamaIndex DSPy
3. LLM APIs: Gemini Bedrock Vertex AI Claude
4. Vector DBs: Pinecone Weaviate (Can be any )
5. Evaluation: LangSmith custom eval pipelines
Key Responsibilities
Build multi-step and multi-agent workflows
Implement RAG pipelines and document retrieval strategies
Design prompt templates system instructions and guardrails
Integrate agents with tools APIs and internal services
Optimize latency accuracy and token usage
Create automated LLM evaluation and regression tests
Required Skills
Strong Python development skills
Hands-on experience with LLMs and embeddings
Solid understanding of prompt engineering and hallucination mitigation
Familiarity with cloud AI services
Role - Sr Gen AI Lead Location - San Jose CA (Onsite) Number of opening :-10 GenAI Engineers build the core intelligence layer-agents workflows prompts and decision logic-that power enterprise AI applications. 60% on Python 20% Agent & RAG Frameworks Agentic AI Python Coding Test- F2F interv...
Role - Sr Gen AI Lead
Location - San Jose CA (Onsite)
Number of opening :-10
GenAI Engineers build the core intelligence layer-agents workflows prompts and decision logic-that power enterprise AI applications.
60% on Python 20% Agent & RAG Frameworks Agentic AI Python Coding Test- F2F interview (Santa Clara CA )
Technology Stack (Priority Order)
1. Languages: Python
2. Agent & RAG Frameworks: LangChain LlamaIndex DSPy
3. LLM APIs: Gemini Bedrock Vertex AI Claude
4. Vector DBs: Pinecone Weaviate (Can be any )
5. Evaluation: LangSmith custom eval pipelines
Key Responsibilities
Build multi-step and multi-agent workflows
Implement RAG pipelines and document retrieval strategies
Design prompt templates system instructions and guardrails
Integrate agents with tools APIs and internal services
Optimize latency accuracy and token usage
Create automated LLM evaluation and regression tests
Required Skills
Strong Python development skills
Hands-on experience with LLMs and embeddings
Solid understanding of prompt engineering and hallucination mitigation
Familiarity with cloud AI services
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