2 of 2 Reinforcement Learning Jobs in the Midlands

An exceptional opportunity for a Machine Learning Engineer (with Full-Stack experience) to join an innovative market leader at the forefront of developing next-generation solutions that transform digital interactions. The role will focus on projects to leverage state-of-the-art generative AI, retrieval-augmented generation (RAG), and reasoning frameworks to build intelligent and context-aware systems. We … are seeking talented Machine Learning Engineers with full-stack software development experience to join our client's team and help shape the future of AI-powered automation. Within this dynamic role varied duties will include: Search relevancy engineering. Conversational AI Development : Design, train, fine-tune, and deploy LLMs with reasoning capabilities. Retrieval-Augmented Generation (RAG): Implement, optimise, and scale … RAG pipelines for effective information retrieval from structured and unstructured sources. Model Fine-Tuning & Training : Train domain-specific models using techniques like LoRA, QLoRA, PEFT, reinforcement learning, and supervised fine-tuning (SFT). Model Deployment & Inferencing : Optimise model serving and inference using vLLM, DeepSpeed, TensorRT, Triton, and other acceleration frameworks. Multi-Agent Systems : Develop and integrate agentic capabilities More ❯

An exceptional opportunity for a Machine Learning Engineer (with Full-Stack experience) to join an innovative market leader at the forefront of developing next-generation solutions that transform digital interactions. The role will focus on projects to leverage state-of-the-art generative AI, retrieval-augmented generation (RAG), and reasoning frameworks to build intelligent and context-aware systems. We … are seeking talented Machine Learning Engineers with full-stack software development experience to join our client's team and help shape the future of AI-powered automation. Within this dynamic role varied duties will include: Search relevancy engineering. Conversational AI Development : Design, train, fine-tune, and deploy LLMs with reasoning capabilities. Retrieval-Augmented Generation (RAG): Implement, optimise, and scale … RAG pipelines for effective information retrieval from structured and unstructured sources. Model Fine-Tuning & Training : Train domain-specific models using techniques like LoRA, QLoRA, PEFT, reinforcement learning, and supervised fine-tuning (SFT). Model Deployment & Inferencing : Optimise model serving and inference using vLLM, DeepSpeed, TensorRT, Triton, and other acceleration frameworks. Multi-Agent Systems : Develop and integrate agentic capabilities More ❯