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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - **Company:** Xantura View All Jobs - **Location:** London, UK - **Experience:** Experienced - **Salary:** £50,000.0 - £70,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Microsoft Azure, Cloud Storage, Computer Programming, Continuous Integration, Python (Programming Language), Key Management, Machine Learning, Azure Machine Learning, Software Engineering, Management of Software Versions, Pytorch, Large Language Models, Backend, Fastapi, Scikit Learn, Kubernetes, Information Technology, HuggingFace, Xgboost, Free and Open-Source Software, Azure AKS, Machine Learning Operations, Text Analysis - **Published:** August 22, 2026 - **Apply:** https://www.careerjet.co.uk/job/gb56c97f39760b644d2571128789a3bc93/eaa ## About the Role * Bachelor's or Master's degree in Computer Science, Machine Learning, or a related technical field - or equivalent practical experience. * 3+ years of professional experience as an ML Engineer, or related role. * Strong programming skills and production experience in Python. * Experience building and maintaining data or ML pipelines with an orchestration tool such as Dagster (or Airflow, Prefect, etc.). * Hands-on experience with common ML libraries and frameworks, e.g. PyTorch, scikit-learn, and gradient-boosting libraries such as XGBoost or LightGBM. * Clear evidence of practical experience defining and deploying containerised systems, i.e.: * Implementing APIs for internal services, e.g. via FastAPI; * Deploying containerised systems to production, in particular via Kubernetes. In addition, the following would be an advantage: * PhD in Computer Science, Machine Learning, or a related field with a strong publication record in text analytics, representation learning, or applied predictive modelling. * Practical experience productionising LLMs, i.e.: * Working with vector databases and developing retrieval-augmented generation (RAG) pipelines - experience setting up/configuring vector DBs, as well as using, would be advantageous; * Finding and productionising recent AI models (e.g. via Huggingface (transformers), OpenAI APIs); * Building agentic systems (e.g. via LangChain, AutoGen, PydanticAI). * Evidence of participating in Open-Source Software (OSS) development, public hackathons, or other sharable coding samples. * Deep expertise in embedding-based architectures, including bi-encoders, cross-encoders, etc. for long-horizon text or temporal prediction tasks. * Practical experience building and serving production-ready, asynchronous APIs for embedding and/or other compute-intensive services. * Proficiency in Python for building high-performance data and model pipelines, with strong software engineering discipline (testing, versioning, CI/CD). * Good familiarity with the Azure ecosystem (Azure Kubernetes Service, Azure Batch, Azure AI Foundry, Azure Machine Learning, Azure Blob Storage, Azure Key Vault) . ## Description In this role you will work in the Platform team - a function for the deployment and evolution of the backend platform that underpins the core of the Xantura business., * Own and advance a predictive modelling platform that scales across problem types and tenants, using it to design, implement, and iterate models (embedding-based sequence encoders, temporal survival models, gradient-boosted decision trees) that predict key vulnerabilities in housing, health, and other social domains. * Track developments in ML and frontier models, running structured experiments to bring promising techniques into production safely. * Build robust evaluation pipelines, training datasets, and model infrastructure to support continuous improvement of natural language & predictive analytics. * Ensure responsible AI deployment, embedding ethical and regulatory considerations into every stage of development. ## Related Videos - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Intro to FastAPI](https://www.wearedevelopers.com/videos/462-intro-to-fastapi) - [TikTok's Privacy Innovation](https://www.wearedevelopers.com/videos/1036-tiktok-s-privacy-innovation) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Multilingual NLP pipeline up and running from scratch](https://www.wearedevelopers.com/videos/901-multilingual-nlp-pipeline-up-and-running-from-scratch) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Why Upskilling And Reskilling is Important For Developers](https://www.wearedevelopers.com/magazine/428-why-upskilling-and-reskilling-is-important-for-developers)