Read everything carefully. The requirements and screening questions are critical and if not answered correctly and satisfactorily will result in auto-rejection and waste of your time.
Work from Home.
This is a full-time role. If you plan to do 2 or more jobs at the same time or want to do this part-time, that won't work for us. In that case please do not apply as it will get auto-rejected
Note - this job requires working late night India time until 4AM to overlap with USA working times. Do not apply if this timing doesn't work
Salary depends on experience and current verifiable (paychecks) compensation.
Junior candidates with 2 years experience are suitable
About Qubrid AI
Qubrid AI is building the next generation AI infrastructure platform that enables organizations to deploy, scale, and monetize AI workloads across cloud, on-premises, and hybrid environments. Our platform combines GPU cloud infrastructure, inference APIs, model deployment services, RAG pipelines, fine-tuning capabilities, and AI orchestration software into a unified AI stack.We are seeking an experienced and hands-on AI Inference Engineer to design, optimize, and scale large-scale AI inference systems supporting thousands of concurrent users and enterprise AI workloads. Role Overview As an AI Inference Engineer, you will be responsible for deploying, optimizing, and operating open-source and commercial AI models across NVIDIA GPU infrastructure. You will work at the intersection of machine learning, distributed systems, GPU optimization, and cloud infrastructure to deliver low-latency, high-throughput AI services. This is a highly technical role requiring deep expertise in LLM serving, GPU performance tuning, model optimization, inference frameworks, and large-scale production deployments.
Responsibilities
AI Model Deployment & Serving
Deploy and manage Large Language Models (LLMs), multimodal models, vision models, speech models, and embedding models in production.
Build and optimize inference pipelines for enterprise and public AI workloads.
Implement scalable serving architectures using modern inference frameworks.
Support model versioning, rollbacks, canary deployments, and A/B testing.
GPU Performance Optimization
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