What are the responsibilities and job description for the AI Support Engineer position at Jobs via Dice?
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Mandatory Skills: AI/ML Platforms, Python
Job Description: The role is for an AI Support Engineer responsible for operational support of enterprise AI platforms. The focus is on ensuring stability, performance, availability, and reliability of AI/ML workloads by working closely with architecture, middleware, and development teams.
The position requires 8–10 years of application or platform support experience with hands-on exposure to AI/ML environments, including AI platforms (e.g., Vertex AI or similar), LLM-based applications, RAG pipelines, AI APIs, Python scripting, and monitoring AI workloads for latency, usage, errors, and model drift.
Experience With Kubernetes And Cloud-native Environments Is Essential.
Key responsibilities include day-to-day operational support, incident triage and resolution related to AI APIs, model serving, pipelines, and data flows; monitoring AI health, performance, quality, and cost; coordinating with platform and middleware teams; supporting deployment and configuration of AI services and agent frameworks; performing root cause analysis, and maintaining runbooks, FAQs, and operational documentation.
Mandatory Skills: AI/ML Platforms, Python
Job Description: The role is for an AI Support Engineer responsible for operational support of enterprise AI platforms. The focus is on ensuring stability, performance, availability, and reliability of AI/ML workloads by working closely with architecture, middleware, and development teams.
The position requires 8–10 years of application or platform support experience with hands-on exposure to AI/ML environments, including AI platforms (e.g., Vertex AI or similar), LLM-based applications, RAG pipelines, AI APIs, Python scripting, and monitoring AI workloads for latency, usage, errors, and model drift.
Experience With Kubernetes And Cloud-native Environments Is Essential.
Key responsibilities include day-to-day operational support, incident triage and resolution related to AI APIs, model serving, pipelines, and data flows; monitoring AI health, performance, quality, and cost; coordinating with platform and middleware teams; supporting deployment and configuration of AI services and agent frameworks; performing root cause analysis, and maintaining runbooks, FAQs, and operational documentation.