Cortex Inference Engineering

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