Real-Time Inference Engineering Lead

We are seeking a Real-Time Inference Engineering Lead for a contract position based in Charlotte, North Carolina. This role offers the opportunity to lead technical design and delivery for highly available, production-grade real-time inference systems over a six-month or longer engagement.

Responsibilities

• Lead technical design and engineering delivery for production inference services with high availability and performance requirements
• Design and operate online inference architectures, model-serving frameworks, and predictive-model deployment patterns
• Design and operate RESTful, gRPC, or event-driven APIs supporting real-time inference workloads
• Implement and optimize autoscaling, resource management, capacity planning, performance testing, and load testing strategies for distributed services
• Establish and maintain observability, monitoring, dashboards, alerts, SLIs, SLOs, and incident-management practices
• Ensure resiliency, high availability, fault tolerance, and disaster recovery for critical services
• Collaborate with data science, ML engineering, platform engineering, application, security, and business teams to align technical solutions with organizational goals
• Oversee CI/CD processes, Git-based development practices, automated testing, deployment automation, and production-release procedures

Qualifications

• Required
• 8+ years of software engineering, platform engineering, cloud engineering, SRE, or infrastructure engineering experience
• 4+ years of experience designing, building, or operating production APIs, distributed systems, platform services, or real-time data and ML workloads
• 4+ years of hands-on experience with Kubernetes and container platforms in production, including GKE, OpenShift, or comparable environments
• Demonstrated experience leading technical design and engineering delivery for highly available, performance-sensitive production services
• Strong experience with online inference architecture, model-serving frameworks, or predictive-model deployment patterns
• Hands-on experience designing and operating RESTful, gRPC, or event-driven APIs
• Experience with autoscaling, resource management, capacity planning, performance testing, and load testing for distributed services
• Experience implementing observability, monitoring, dashboards, alerts, SLIs, SLOs, and incident-management practices
• Experience with CI/CD, Git-based development, automated testing, deployment automation, and production-release practices
• Strong understanding of resiliency, high availability, fault tolerance, disaster recovery, and operational support for critical services
• Ability to work effectively with data science, ML engineering, platform engineering, application teams, security, and business stakeholders