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# Machine Learning Engineer

- **Company:** ConnexAI
- **Location:** Manchester, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Codecs, Python (Programming Language), Machine Learning, Chatbots, Pytorch, Large Language Models, Generative AI, Information Technology, HuggingFace, Machine Learning Operations, Speech Synthesis, TensorRT
- **Published:** September 15, 2026
- **Apply:** https://www.collegerecruiter.com/job/2879166255-machine-learning-engineer

## About the Role

* MSc or PhD in Computer Science or a related field.
 * 3-5 years of hands-on experience deploying and scaling machine learning solutions in production.
 * Strong Python programming skills.
 * Proven experience in deploying and optimising LLMs/transformers in production environments.
 * Knowledge of LLM inference servers (e.g., Triton, TensorRT, TorchServe).
 * Experience with GPU scaling for large-scale machine learning models.
 * Expertise in deploying complex machine learning pipelines in production environments.

Desirable Skills:

 * Proficiency with PyTorch and Hugging Face transformers.
 * Experience with neural audio codecs (e.g., Encodec).
 * Background in Text-to-Speech (TTS) development.
 * Experience with advanced techniques such as Residual Vector Quantization (RVQ), Generative Adversarial Networks (GANs), and diffusion models.

## Description

We are at the forefront of revolutionising Text-to-Speech (TTS) and Speech Synthesis in Conversational AI, and we're looking for a skilled Senior Machine Learning Engineer to join our expanding team., As a Senior Machine Learning Engineer, you will be instrumental in deploying state-of-the-art Text-to-Speech models. You will be responsible for scaling and optimising TTS systems, ensuring they are production-ready and capable of running efficiently on large-scale deployments., * Collaborate closely with the TTS team to deploy and scale advanced models in production environments.

 * Lead efforts in optimizing TTS pipelines for performance and scalability, particularly focusing on GPU utilisation.
 * Implement and maintain LLM (Large Language Models) and transformers, ensuring efficient inference on a large scale.
 * Integrate and manage LLM-based inference servers like Triton, TensorRT, or TorchServe to streamline model deployment and scaling.
 * Work on deploying complex pipelines in production, ensuring seamless integration with existing systems.


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