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.

• 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

• 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, Open Shift, 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