Salesforce Lead developer
Diamondpick, Nashville, TN, United States
Update on Apr 20: New role urgently need strong and genuine profiles.
Job Title:
AI Support Engineer Location:
Nashville, TN (Onsite from day one) Duration:
12+ Month
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.
Job Title:
AI Support Engineer Location:
Nashville, TN (Onsite from day one) Duration:
12+ Month
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.