Machine Learning Engineer - Conversational AI & MLOps

Robert Walters is exclusively partnering with Connect Managed Services to recruit a Machine 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-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.

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.

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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