More jobs:
AI Engineer/Architect
Job in
Minneapolis, Hennepin County, Minnesota, 55400, USA
Listed on 2026-07-18
Listing for:
Diverse Lynx
Seasonal/Temporary
position Listed on 2026-07-18
Job specializations:
-
IT/Tech
AI Engineer (Applied/Software), SRE/Site Reliability, Cloud Computing: Infrastructure & Operations
Job Description & How to Apply Below
Job Position: AI Architect/Engineer
Job Location: Chicago, IL or Minneapolis, MN
Job Duration: Long Term Contract
Job Description:
- Strong expertise in AI Ops / MLOps / LLM Ops practices
- End-to-end model lifecycle management
- Advanced model monitoring, observability, and alerting frameworks
- Drift detection, performance tracking, and automated retraining
- CI/CD and Dev Sec Ops for AI/ML systems
- Scalable deployment architectures for AI/ML and LLM
- AI governance, risk, security, and compliance frameworks [
- AI platform engineering and operational tooling experience
- Performance optimization (latency, cost, scalability) for AI workloads
- Strong experience in automation of AI operations workflows
- Data pipeline integration and ML infrastructure management
- Cross-functional collaboration with engineering, data, and platform teams
Responsibilities:
- Define and implement AI Ops / MLOps / LLM Ops strategy for enterprise AI platforms
- Manage end-to-end AI operations lifecycle (deployment, monitoring, scaling, optimization)
- Establish model monitoring, observability, and alerting frameworks for production AI systems
- Implement model lifecycle management (versioning, deployment, retraining, rollback, drift detection)
- Define and track AI Ops KPIs (performance, reliability, incident reduction, automation efficiency)
- Ensure high availability, scalability, and performance of AI systems in production
- Drive adoption of CI/CD and Dev Sec Ops practices for AI/ML systems
- Implement governance, risk, security, and compliance controls for AI systems
- Collaborate with AI engineering, data, and platform teams for seamless operation
- Manage incident response, root-cause analysis, and continuous improvement for AI system
- Optimize cost, latency, and resource utilization of AI workloads
- Drive automation of AI operations processes and workflows
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