Snowflake 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* 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. A successful candidate combines AI platform engineering, distributed systems expertise, and operational excellence to deliver resilient, high-performance inference platforms that enable enterprise-scale real-time AI solutions.