ml engineer for broadband connectivity

Hyperoptic is a telecommunications provider focused on delivering broadband services and gigabit connectivity.

• Design, train, validate, and deploy machine learning models across use cases;
• Integrate ML solutions into business systems, including CRM, OSS, digital platforms, and APIs, supporting real-time and batch inference;
• Move models from experimentation into production through APIs, Kafka pipelines, and streaming architectures;
• Build and maintain MLOps pipelines for model CI/CD, versioning, deployment automation, and monitoring;
• Ensure reproducibility, traceability, and governance of models and experiments;
• Collaborate with Data Engineers to define scalable data pipelines and feature engineering logic;
• Design, implement, and operationalise AI pipelines with LLM integrations, RAG systems, embedding workflows, and agentic AI automation;
• Build reusable AI components and maintain model registries, prompt libraries, and evaluation datasets;
• Establish evaluation frameworks, observability tooling, and guardrails for model quality, latency, and cost;
• Apply responsible AI practices covering bias mitigation, explainability, transparency, data privacy, and security;
• Own production monitoring and incident response for AI/ML systems;
• Translate business problems into AI/ML solutions with clear success metrics;
• Champion AI/ML best practices across the engineering organisation;
• Contribute to internal standards, frameworks, and reusable components;
• Mentor colleagues working with AI/ML capabilities.

• Bachelor's degree in Computer Science, Software Engineering, Mathematics, or a related field;
• Previous software or data engineering experience, including building, deploying, and operating ML models or AI systems in production;
• Strong Python programming skills with pandas, scikit-learn, PyTorch, or TensorFlow;
• Experience with AWS, Spark, and SQL;
• Knowledge of the full ML lifecycle from experimentation to deployment and monitoring;
• Experience with Kafka, APIs, microservices, and Docker/Kubernetes;
• Hands-on experience with MLOps tooling such as SageMaker or MLflow;
• Experience with Airflow or equivalent orchestration tools, CI/CD practices, and version control;
• Understanding of LLM frameworks, prompt engineering, RAG architectures, and vector stores;
• Strong understanding of data modelling and feature engineering;
• Experience working with large-scale structured and unstructured data;
• Ability to connect ML/AI solutions to business outcomes and communicate complex concepts clearly to non-technical stakeholders;
• Nice to have: Experience with agentic AI frameworks such as CrewAI, AutoGen, or Claude Code; fine-tuning open-source models; deep understanding of LLM evaluation and responsible AI governance practices.

• Competitive salary;
• Global remote working for up to 2 weeks per year for employees able to work remotely;
• 25 Days' paid holiday, increasing annually up to 35 days;
• Extra days off for birthdays, moving house, and volunteering;
• Access to the Rezilient wellbeing platform;
• Private medical insurance for permanent employees;
• Fit Pass discount;
• Technology discount;
• Team sports activities;
• Massages in the office;
• Sweet Tuesdays.