ConnexAI
Machine Learning Engineer
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Role: Machine Learning Engineer
Location: Manchester, UK (Hybrid)
About the job
Can you see yourself revolutionising the Agentic AI industry? We are a multi-award-winning AI and SaaS provider based in Manchester, dedicated to boosting productivity and efficiency across our global customer base spanning five continents. As a Machine Learning Engineer, you will be the architect responsible for maximising the fluidity, scale, and performance of our ASR and TTS products within the Agentic AI pipeline.
This role offers a world-class research and production environment: you will work alongside like-minded scientists who develop state-of-the-art models, acting as the vital link between experimental research and enabling these bespoke systems to be used by our customers.
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Your primary focus will be implementing highly efficient inference pipelines for real-time and offline speech recognition and synthesis, ensuring our Agentic vision translates into a seamless user experience.
Responsibilities
- Liaise with ASR and TTS technical leads, software engineers, and DevOps teams to deploy models efficiently on Hopper and Blackwell GPU architectures.
- Maintain steady biweekly progression within our sprint-based research environment.
- Implement low-latency, real-time inference ML pipelines using tools such as Triton Inference Server, vLLM, or SGLang.
- Optimise model performance across cloud platforms using frameworks like TensorRT and ONNX.
- Build and maintain robust API services using Python-based web frameworks (e.g., FastAPI).
- Manage containerisation and orchestration workflows using Docker and Kubernetes.
- Ensure system reliability through observability and monitoring tools like Prometheus, Grafana, and OpenTelemetry.
- Write concise technical documentation and research papers.
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Requirements
- MSc plus 2+ years of hands-on experience in Speech or Generative AI.
- Deep understanding of Generative AI, Neural Networks, and the latest LLM architectures.
- Expert-level proficiency in Python and PyTorch.
- Proven experience in performance optimisation and cloud platform deployment.
- Strong background in containerisation and orchestration (Docker, K8s, etc.).
- Demonstrated ability to deploy and scale low-latency ML pipelines in production.
- Strong oral and written communication skills.
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