Robert Walters
Machine Learning Engineer - Conversational AI & MLOps
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Robert Walters is exclusively partnering with Connect Managed Services to recruit a Machine Learning Engineer
This is a hands-on engineering position sitting at the intersection of Machine Learning, Generative AI and production engineering, with particular focus on deploying and optimising speech and language models across cloud and edge environments.
The successful candidate will work on production-grade ASR, TTS, LLM and Small Language Model pipelines, taking AI capabilities from development through to highly available, low-latency production environments.
The Role
As a Machine Learning Engineer, you will be responsible for building and optimising scalable AI platforms capable of supporting real-time conversational applications.
Key responsibilities will include:
- Designing and deploying production-grade, low-latency Automatic Speech Recognition (ASR), Text-to-Speech (TTS), LLM and Small Language Model (SLM) pipelines.
- Building high-performance asynchronous REST and WebSocket APIs using FastAPI to support real-time conversational AI applications.
- Deploying machine learning workloads across AWS, Azure, GCP and on-premise/bare-metal infrastructure.
- Designing automated MLOps and CI/CD pipelines covering model testing, versioning, deployment and monitoring.
- Containerising AI applications using Docker or Podman and supporting consistent deployment across development, staging and production.
- Optimising GPU utilisation across both single-GPU and distributed multi-GPU environments.
- Improving Python and model inference performance using technologies including NumPy, Numba, Triton and CUDA-based libraries.
- Conducting load and stress testing to ensure AI services remain performant and stable under high levels of concurrent traffic.
- Optimising cloud infrastructure to balance model performance, scalability and compute cost.
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What We're Looking For
You will have strong software engineering foundations alongside demonstrable experience deploying machine learning models into production environments.
Essential experience includes:
- Strong commercial development experience with Python, including asynchronous programming.
- Strong knowledge of the Python machine learning ecosystem, particularly PyTorch, Scikit-learn and NumPy.
- Experience deploying speech technologies, ideally including both ASR and TTS models.
- Experience deploying, serving or optimising Large Language Models or Small Language Models.
- Strong understanding of production MLOps, model deployment and CI/CD practices.
- Experience with container technologies including Docker and/or Podman.
- Practical cloud experience across one or more of AWS, Azure or GCP, ideally using services such as SageMaker, Azure ML or Vertex AI.
- Experience with CI/CD and MLOps tooling such as GitLab CI, GitHub Actions, Jenkins, Kubeflow or MLflow.
- Exposure to accelerating Python or machine learning workloads using technologies such as Numba or Triton.
- Understanding of GPU-based machine learning infrastructure and performance optimisation.
Desirable Experience
Additional experience in any of the following areas would be advantageous:
- Conversational AI and dialogue management.
- Prompt engineering and Retrieval-Augmented Generation (RAG).
- Real-time data streaming platforms such as Kafka.
- Vector databases including Pinecone, Milvus or Qdrant.
- Model compression and optimisation techniques including INT8/FP4 quantisation, pruning and knowledge distillation.
- Deploying machine learning models to resource-constrained or edge environments.
- Distributed GPU inference and high-performance model serving.
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Why Consider This Opportunity?
This position offers the opportunity to work directly on technically challenging, production-focused AI systems rather than purely experimental machine learning projects.
You will have exposure across the complete AI engineering lifecycle, including model serving, cloud infrastructure, GPU optimisation, MLOps, APIs and real-time conversational technology, within an environment where performance and scalability are central to the product.
Salary: £70,000 - £90,000 depending on experience.
To discuss the opportunity confidentially or receive further information, apply through Robert Walters.
Desired Skills and Experience
Machine Learning Engineering, Python, PyTorch, Scikit-learn, NumPy, MLOps, Large Language Models (LLMs), Small Language Models (SLMs), Conversational AI, Automatic Speech Recognition (ASR), Text-to-Speech (TTS), FastAPI, REST APIs, WebSockets, Docker, Podman, AWS, Microsoft Azure, Google Cloud Platform (GCP), GPU Computing, CUDA, Numba, Triton, CI/CD, MLflow, Kubeflow, GitHub Actions, GitLab CI, Jenkins, Retrieval-Augmented Generation (RAG), Prompt Engineering, Kafka, Vector Databases, Model Quantisation, Distributed Computing, production deployment of machine learning models and AI services, low-latency real-time inference pipelines, ASR and TTS deployment, LLM and SLM serving and optimisation, asynchronous API development, MLOps pipeline design, containerised ML deployment, hybrid cloud and bare-metal infrastructure, GPU performance optimisation, model compression, load testing and high-concurrency AI systems.
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