AI Engineer, AI Services
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仕事内容
Role Overview: The AI Engineer is a hands-on builder who develops and deploys agentic AI solutions for client-facing projects, focusing on multi-agent workflows, retrieval pipelines, and system integrations.
Key Responsibilities: Design agentic solutions using frameworks such as LangChain, LangGraph, Autogen, LlamaIndex, or CrewAI, and implement components for document processing, retrieval, and structured output.
Data & Retrieval: Develop and maintain vector database pipelines (Pinecone, pgvector, Elasticsearch) and create RAG-based retrieval workflows, while configuring LLM/SLM endpoints, tool-calling, and memory/state handling.
Workflow & Integrations: Implement real-time and batch workflows with Python and tools like Kafka, EventBridge, Airflow, Snowflake, S3, and n8n; build integrations with enterprise systems (Jira, ServiceNow, SharePoint, Salesforce) and APIs.
Quality & Observability: Write tests, contribute to monitoring, logging, metrics, and guardrail configurations; participate in debugging sessions and post-incident reviews.
Client Delivery & Governance: Participate in workshops and demos, translate requirements into technical tasks, document deployment steps, and adhere to security, governance, and Responsible AI practices.
Key Responsibilities: Design agentic solutions using frameworks such as LangChain, LangGraph, Autogen, LlamaIndex, or CrewAI, and implement components for document processing, retrieval, and structured output.
Data & Retrieval: Develop and maintain vector database pipelines (Pinecone, pgvector, Elasticsearch) and create RAG-based retrieval workflows, while configuring LLM/SLM endpoints, tool-calling, and memory/state handling.
Workflow & Integrations: Implement real-time and batch workflows with Python and tools like Kafka, EventBridge, Airflow, Snowflake, S3, and n8n; build integrations with enterprise systems (Jira, ServiceNow, SharePoint, Salesforce) and APIs.
Quality & Observability: Write tests, contribute to monitoring, logging, metrics, and guardrail configurations; participate in debugging sessions and post-incident reviews.
Client Delivery & Governance: Participate in workshops and demos, translate requirements into technical tasks, document deployment steps, and adhere to security, governance, and Responsible AI practices.
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