MLops Engineer/ AI Inference Engineering Lead

Hello Everyone,

Position: #MLops_Engineer/ #AI_Inference_Engineering_Lead

Location: Charlotte, NC

Note: Mention Visa and Current location when you reach out to me.

Description:

The Real-Time Inference Engineering Lead is responsible for designing, building, and operationalizing scalable, low-latency model serving platforms that power real-time AI and predictive decisioning use cases. This role leads the architecture, deployment, performance optimization, resiliency, and operational governance of online inference services across cloud and on-premises environments.

·     Design and implement highly available, low-latency model serving architectures for real-time inference workloads.

·     Develop and standardize deployment patterns for scalable AI/ML services across Kubernetes-based platforms.

·     Lead API-based inference service design, integration, and lifecycle management.

·     Optimize model serving performance through latency tuning, caching strategies, autoscaling, and resource management.

·     Establish monitoring, observability, SLOs/SLAs, alerting, and operational runbooks for production services.

·     Drive load testing, capacity planning, resiliency engineering, and disaster recovery readiness.

·     Integrate inference platforms with CI/CD pipelines to enable automated deployments and controlled releases.

·     Partner with Data Science, Platform Engineering, MLOps, and Infrastructure teams to ensure reliable production model operations.

·     Govern operational best practices, security, reliability, and performance standards for enterprise AI deployments.

·     Online inference and model-serving architectures

·     Real-time APIs and distributed systems design

·     Kubernetes, container orchestration, and service mesh technologies

·     Autoscaling, capacity management, and workload optimization

·     Performance engineering, load testing, and latency optimization

·     Monitoring, observability, logging, tracing, and SLO management

·     CI/CD, DevOps, and Infrastructure-as-Code practices

·     Reliability engineering, fault tolerance, and resiliency patterns

·     Cloud and on-premises platform operations

·     Python, Java, Go, or similar backend development experience

·     Experience with MLOps platforms and enterprise AI deployment frameworks.

·     Hands-on experience with real-time recommendation, fraud, risk, personalization, or predictive analytics platforms.

·     Familiarity with GPU-based inference, model optimization, and multi-cloud deployments.

Regards