T-Pro

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&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. Responsibilities API Development Design, build, and maintain production-ready APIs for speech, language, and other AI models. Provide SDKs and documentation to enable easy developer adoption. Model Deployment Deploy models (ASR, LLM, and others) using Triton Inference Server or similar systems. Optimise inference pipelines for low-latency, high-throughput workloads. Scalability & Reliability Architect infrastructure for handling large-scale, concurrent inference requests. Implement monitoring, logging, and auto-scaling for deployed services. Collaboration Work with research teams to productionize new models. Partner with application teams to deliver AI functionality seamlessly through APIs. DevOps & Infrastructure Automate CI/CD pipelines for models and APIs. Manage GPU-based infrastructure in cloud or hybrid environments. Requirements Core Skills Strong programming experience in Python (FastAPI, Flask) and/or Go/Node.js for API services. Hands-on experience with model deployment using Triton Inference Server, TorchServe, or similar. Familiarity with both ASR frameworks and LLM frameworks (Hugging Face Transformers, TensorRT-LLM, vLLM, etc.). Infrastructure Experience with Docker, Kubernetes, and managing GPU-accelerated workloads. Deep knowledge of real-time inference systems (REST, gRPC, WebSockets, streaming). Cloud experience (AWS, GCP, Azure). Bonus Experience with model optimisation (quantisation, distillation, TensorRT, ONNX). Exposure to MLOps tools for deployment and monitoring