AI Engineer or Real-Time Inference Engineering Lead

Job Title: AI Engineer or  Real-Time Inference Engineering Lead

Location: Concord, California

Type : Contract

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Real-Time Inference Engineering Lead

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 deaccessioning 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, auto scaling, and resource management.

·       Establish monitoring, observability, SLOs/SLAs, alerting, and operational run books 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, Mops, 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

·       Auto scaling, 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.