Qureos Mentor

T-Pro

<div>We are seeking an AI Platform Engineer to build and scale the infrastructure that powers our production AI services. You will take cutting-edge models-ranging from speech recognition (ASR) to large language models (LLMs), and deploy them into highly available, developer-friendly APIs.
You will be responsible for creating the bridge between the R&amp;D team, who train models, and the applications that consume them. This means developing robust APIs, deploying and optimising models on Triton Inference Server (or similar frameworks), and ensuring real-time, scalable inference.<h2 class="jobSectionHeader"> 
Responsibilities</h2> 
API Development<ul><li>
Design, build, and maintain production-ready APIs for speech, language, and other AI models.</li><li>
Provide SDKs and documentation to enable easy developer adoption.</li></ul> 
Model Deployment<ul><li>
Deploy models (ASR, LLM, and others) using Triton Inference Server or similar systems.</li><li>
Optimise inference pipelines for low-latency, high-throughput workloads.</li></ul> 
Scalability &amp; Reliability<ul><li>
Architect infrastructure for handling large-scale, concurrent inference requests.</li><li>
Implement monitoring, logging, and auto-scaling for deployed services.</li></ul> 
Collaboration<ul><li>
Work with research teams to productionize new models.</li><li>
Partner with application teams to deliver AI functionality seamlessly through APIs.</li></ul> 
DevOps &amp; Infrastructure 
<ul><li>
Automate CI/CD pipelines for models and APIs.</li><li>
Manage GPU-based infrastructure in cloud or hybrid environments.</li></ul><h2 class="jobSectionHeader"> 
Requirements</h2> 
Core Skills<ul><li>
Strong programming experience in Python (FastAPI, Flask) and/or Go/Node.js for API services.</li><li>
Hands-on experience with model deployment using Triton Inference Server, TorchServe, or similar.</li><li>
Familiarity with both ASR frameworks and LLM frameworks (Hugging Face Transformers, TensorRT-LLM, vLLM, etc.).</li></ul> 
Infrastructure<ul><li>
Experience with Docker, Kubernetes, and managing GPU-accelerated workloads.</li><li>
Deep knowledge of real-time inference systems (REST, gRPC, WebSockets, streaming).</li><li>
Cloud experience (AWS, GCP, Azure).</li></ul> 
Bonus<ul><li>
Experience with model optimisation (quantisation, distillation, TensorRT, ONNX).</li><li>
Exposure to MLOps tools for deployment and monitoring</li></ul></div>

AI Platform Engineer

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AI Platform Engineer