Inference Lead

Real-Time Inference Engineering Lead

Hybrid Onsite - local candidates preferred. Real-Time Services – Real-Time Inference Engineering Lead

Experience:

8–12 years

Role

Summary:

The Real-Time Inference Engineering Lead will design and industrialize low-latency, resilient model-serving services for predictive AI use cases. The role will define deployment patterns, capacity controls, monitoring, performance standards, and operational practices across cloud and on-premises environments.

Key Responsibilities:

• Architect low-latency online inference and real-time model-serving solutions.
• Develop scalable APIs, microservices, and deployment patterns for predictive models.
• Implement Kubernetes-based deployment, autoscaling, load balancing, and traffic-management strategies.
• Conduct benchmarking, performance tuning, capacity planning, and load testing.
• Optimize latency, throughput, resource consumption, availability, and cost.
• Define monitoring, alerting, SLOs, runbooks, and incident-response practices.
• Build CI/CD pipelines for repeatable model and service releases.
• Design resilience, failover, rollback, disaster recovery, and graceful-degradation patterns.
• Lead technical reviews and mentor inference and platform engineers.

Required Skills:

• Online inference and real-time model-serving architecture.
• REST/gRPC APIs and distributed microservices.
• Kubernetes, containers, autoscaling, and traffic management.
• Performance engineering, latency optimization, and load testing.
• Monitoring, SLOs, capacity planning, and production operations.
• CI/CD and progressive-deployment approaches.
• Resilience and high-availability engineering.
• Cloud and on-premises deployment experience.

Preferred Qualifications:

• Degree in computer science, engineering, or a related discipline.
• Experience with enterprise model-serving platforms and inference runtimes.
• Cloud, Kubernetes, SRE, or ML engineering certification.