Inference Lead (Machine Learning Platform Engineer Lead Real-Time Inference)

Stellent IT LLC

Charlotte, NC, United States

4 days ago

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Role details

Contract type

Temporary contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Application Programming Interfaces (APIs) Cloud Computing Continuous Integration Disaster Recovery Load Testing Machine Learning Performance Tuning Reliability Engineering Load Balancing Autoscaling Kubernetes Information Technology

+2 more

Low Latency Microservices

Job description

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.

Requirements

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., * 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.