AI Cortex Real-Time Inference Engineering Lead

Job Title: AI Cortex Real-Time Inference Engineering Lead (AI Engineer)

Location: Concord, CA

Job Details:

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.

Regards

Andrew

[email protected]