Machine Learning Engineer - Conversational AI & MLOps

Robert Walters is exclusively partnering with Connect Managed Services to recruit aMachine Learning Engineer to help design, deploy and scale the next generation of Conversational AI and data analytics platforms.

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-gradeASR, TTS, LLM and Small Language Model pipelines, taking AI capabilities from development through to highly available, low-latency production environments.

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-latencyAutomatic Speech Recognition (ASR), Text-to-Speech (TTS), LLM and Small Language Model (SLM) pipelines.
• Building high-performance asynchronousREST and WebSocket APIs using FastAPI to support real-time conversational AI applications.
• Deploying machine learning workloads acrossAWS, Azure, GCP and on-premise/bare-metal infrastructure.
• Designing automatedMLOps and CI/CD pipelines covering model testing, versioning, deployment and monitoring.
• Containerising AI applications usingDocker or Podman and supporting consistent deployment across development, staging and production.
• Optimising GPU utilisation across bothsingle-GPU and distributed multi-GPU environments.
• Improving Python and model inference performance using technologies includingNumPy, 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.

You will have strong software engineering foundations alongside demonstrable experience deploying machine learning models into production environments.

Essential experience includes:

• Strong commercial development experience withPython, including asynchronous programming.
• Strong knowledge of the Python machine learning ecosystem, particularly PyTorch, Scikit-learn and NumPy.
• Experience deployingspeech technologies, ideally including both ASR and TTS models.
• Experience deploying, serving or optimisingLarge Language Models or Small Language Models.
• Strong understanding of productionMLOps, model deployment and CI/CD practices.
• Experience with container technologies includingDocker and/or Podman.
• Practical cloud experience across one or more ofAWS, Azure or GCP, ideally using services such as SageMaker, Azure ML or Vertex AI.
• Experience with CI/CD and MLOps tooling such asGitLab CI, GitHub Actions, Jenkins, Kubeflow or MLflow.
• Exposure to accelerating Python or machine learning workloads using technologies such asNumba or Triton.
• Understanding of GPU-based machine learning infrastructure and performance optimisation.

Additional experience in any of the following areas would be advantageous:

• Conversational AI and dialogue management.
• Prompt engineering andRetrieval-Augmented Generation (RAG).
• Real-time data streaming platforms such asKafka.
• Vector databases includingPinecone, Milvus or Qdrant.
• Model compression and optimisation techniques includingINT8/FP4 quantisation, pruning and knowledge distillation.
• Deploying machine learning models to resource-constrained or edge environments.
• Distributed GPU inference and high-performance model serving.

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, includingmodel 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.

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