26 to 50 of 148 MLOps Jobs in the UK excluding London

researchers to implement novel data processing pipelines Develop an easy-to-use, secure, and robust developer experience for researchers and engineers Contribute to the MLOps best practices at Lila Sciences and write technical documentation for staff Qualifications: 3+ years of experience in software engineering, with a focus in data engineering More ❯

for AI model deployment. Data Visualisation: Ability to present insights effectively using tools like Matplotlib, Seaborn, Tableau, or Power BI. Model Deployment: Understanding of MLOps principles, APIs, and containerization (Docker, Kubernetes) for productionizing AI models. Business-Focused AI Application: Ability to translate AI solutions into measurable business impact. Why Wowcher More ❯

happen. Familiarity with ML experiment tracking and collaboration tools, such as Mlflow and Weights & Biases. A solid background in software engineering and DevOps practices, MLOps deployment, and infrastructure. A knack for generative AI frameworks and SDKs, like RAG, Langchain/LangGraph, Semantic Kernel, and tools such as MS tooling, Co More ❯

cross-functional working environment Possess a clear passion for data science beyond the day job Nice-to-haves: NLP, OpenCV, Generative AI, data visualization, MLOps On offer : Base salary of £85k - £95k depending on experience Hybrid working - 3 days in the Bristol office More ❯

development ✔ Strong Python, SQL, and Databricks skills (especially PySpark & Delta Lake) ✔ Deep experience with Azure: Data Factory, Synapse, Event Hub, Azure Functions ✔ Understanding of MLOps tooling like MLflow and integration with AI pipelines ✔ Exposure to data for LLM workflows, unstructured sources, and real-time ingestion ✔ Comfortable operating in an early More ❯

processes Familiarity with cloud platforms, particularly Microsoft Azure Understanding of microservices architecture and distributed systems Experience with DevOps practices for AI/ML workflows (MLOps) Strong programming skills in Python and related data science libraries Demonstrated ability to work within solution architecture frameworks Other preferred skills Experience with multiple cloud More ❯

Kafka. Strong understanding of SQL, NoSQL, and data modeling. Familiarity with cloud platforms (AWS, Azure, GCP) for deploying ML and data solutions. Knowledge of MLOps practices and tools, such as MLflow or Kubeflow. Strong problem-solving skills, with the ability to troubleshoot both ML models and data systems. A collaborative More ❯

or digital transformation • Certification in MS Azure or AWS Cloud data engineering or similar track • Understanding of the Software Development Life Cycle, Devops and MLOPS • Strong skills in one object orientated language • Excellent presentation skills • Understanding application of project mgmt. and agile methodologies What we look for We're interested More ❯

in the field. You'll join a global team with a distributed set of skills including Research, Applied AI and Engineering. They are seeking MLOps Engineers to help architect the future of communication through AI. This isn't just another engineering role – it's an opportunity to pioneer systems that … transform how companies connect with their customers What You’ll Be Doing You'll be designing and optimising production-grade MLOps pipelines that bring cutting-edge Generative AI and LLMs from experimentation to real-world impact. Your expertise will directly influence how some of the world's leading brands enhance … their strategies. What You'll Build Production-Ready GenAI Infrastructure : Design and deploy scalable MLOps pipelines specifically optimized for GenAI applications and large language models State-of-the-Art Model Deployment : Implement and fine-tune advanced models like GPT and similar architectures in production environments Hybrid AI Systems : Create solutions More ❯

learn. Expert-level experience in Power BI for advanced visualisations, ML model interpretation, and KPI tracking. Deep knowledge of feature engineering, model deployment, and MLOps best practices. Experience with big data processing (Spark, Hadoop) and cloud-based data science environments. Other: Ability to integrate ML workflows into large-scale data More ❯

systems Practical experience with LLMs and their applications Proficiency in Python and ML frameworks (TensorFlow, PyTorch, etc.) Knowledge of containerization, CI/CD, and MLOps practices More ❯

learn. Expert-level experience in Power BI for advanced visualisations, ML model interpretation, and KPI tracking. Deep knowledge of feature engineering, model deployment, and MLOps best practices. Experience with big data processing (Spark, Hadoop) and cloud-based data science environments. Other: Ability to integrate ML workflows into large-scale data More ❯

years in Data Science/ML Engineering 2+ years hands-on with Databricks Strong track record delivering production-grade ML models Solid grasp of MLOps best practices Confident speaking to technical and non-technical stakeholders 🛠️ Tech you’ll be using: Python, SQL, Spark, R MLflow, vector databases GitHub/GitLab More ❯

such as Scikit learn, Keras, Tensor flow, and PySpark. Experience and understanding of mixed technical teams such as engineering, architects, business analysts. Familiar with MLOps industry best practices. Good stakeholder communication skills with proven ability to translate complex scientific findings to non-technical stakeholders. Understanding of the financial industry, in More ❯

such as Scikit learn, Keras, Tensor flow, and PySpark. Experience and understanding of mixed technical teams such as engineering, architects, business analysts. Familiar with MLOps industry best practices. Good stakeholder communication skills with proven ability to translate complex scientific findings to non-technical stakeholders. Understanding of the financial industry, in More ❯

such as Scikit learn, Keras, Tensor flow, and PySpark. Experience and understanding of mixed technical teams such as engineering, architects, business analysts. Familiar with MLOps industry best practices. Good stakeholder communication skills with proven ability to translate complex scientific findings to non-technical stakeholders. Understanding of the financial industry, in More ❯

such as Scikit learn, Keras, Tensor flow, and PySpark. Experience and understanding of mixed technical teams such as engineering, architects, business analysts. Familiar with MLOps industry best practices. Good stakeholder communication skills with proven ability to translate complex scientific findings to non-technical stakeholders. Understanding of the financial industry, in More ❯

such as Scikit learn, Keras, Tensor flow, and PySpark. Experience and understanding of mixed technical teams such as engineering, architects, business analysts. Familiar with MLOps industry best practices. Good stakeholder communication skills with proven ability to translate complex scientific findings to non-technical stakeholders. Understanding of the financial industry, in More ❯

such as Scikit learn, Keras, Tensor flow, and PySpark. Experience and understanding of mixed technical teams such as engineering, architects, business analysts. Familiar with MLOps industry best practices. Good stakeholder communication skills with proven ability to translate complex scientific findings to non-technical stakeholders. Understanding of the financial industry, in More ❯

such as Scikit learn, Keras, Tensor flow, and PySpark. Experience and understanding of mixed technical teams such as engineering, architects, business analysts. Familiar with MLOps industry best practices. Good stakeholder communication skills with proven ability to translate complex scientific findings to non-technical stakeholders. Understanding of the financial industry, in More ❯

such as Scikit learn, Keras, Tensor flow, and PySpark. Experience and understanding of mixed technical teams such as engineering, architects, business analysts. Familiar with MLOps industry best practices. Good stakeholder communication skills with proven ability to translate complex scientific findings to non-technical stakeholders. Understanding of the financial industry, in More ❯

such as Scikit learn, Keras, Tensor flow, and PySpark. Experience and understanding of mixed technical teams such as engineering, architects, business analysts. Familiar with MLOps industry best practices. Understanding of the financial industry, in particular insurance, would be advantageous. If you are interested in this opportunity get in touch today More ❯

and client audiences Nice-to-Have: Experience with marketing data, customer-level modelling, or decision science (e.g. uplift, attribution, causal AI, optimization) Familiarity with MLOps tooling (MLflow, FastAPI, Airflow, etc.) Experience designing and interpreting A/B tests or other experimental frameworks Background in consulting, agency, or fast-paced environments More ❯

such as Scikit learn, Keras, Tensor flow, and PySpark. ·Experience and understanding of mixed technical teams such as engineering, architects, business analysts. ·Familiar with MLOps industry best practices. ·Understanding of the financial industry, in particular insurance, would be advantageous. If you are interested in this opportunity get in touch today More ❯

such as Scikit learn, Keras, Tensor flow, and PySpark. Experience and understanding of mixed technical teams such as engineering, architects, business analysts. Familiar with MLOps industry best practices. Understanding of the financial industry, in particular insurance, would be advantageous. If you are interested in this opportunity get in touch today More ❯