TECHNOGEN, Inc. is a Proven Leader in providing full IT Services, Software Development and Solutions for 15 years.
TECHNOGEN is a Small & Woman Owned Minority Business with GSA Advantage Certification. We have offices in VA; MD & Offshore development centers in India. We have successfully executed 100+ projects for clients ranging from small business and non-profits to Fortune 50 companies and federal, state and local agencies.
Hi,
Greetings of the day!
We are looking to Hire a Talented Professional for the below Job opportunity with one of our clients,
If you're interested, please share your updated resume at your earliest convenience, and I'll be happy to provide more details about the role.
Position: Inference Lead
Location: Charlotte, NC (Hybrid-local candidates only)
Duration: Long Term Contract
Note: Real-Time Services Real-Time Inference Engineering Lead
Experience: 8 12 years
Job Description:
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.
Ranjitha P | Sr. IT Recruiter
