51 to 75 of 324 MLOps Jobs in the UK

cover: Foundations of machine learning AI literacy Ensemble methods & model optimisation Databases & big data Neural networks & deep learning LLM foundations, engineering, customisation and integration MLOps & cloud computing Its all live lectures, tutor-led group work and real-world challenges from day one. Learn it. Apply it. Keep going Your personal ...

platforms (AWS, Azure, or GCP) and ML deployment strategies. Strong communication skills and experience mentoring or leading technical initiatives. Nice to Have Experience with MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI). Familiarity with containerization and orchestration (Docker, Kubernetes). Experience working with large-scale datasets or real-time ...

platforms (AWS, Azure, or GCP) and ML deployment strategies. Strong communication skills and experience mentoring or leading technical initiatives. Nice to Have Experience with MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI). Familiarity with containerization and orchestration (Docker, Kubernetes). Experience working with large-scale datasets or real-time ...

Nice to Have Experience with containerization (Docker, Kubernetes). Exposure to DevOps tools (CI/CD pipelines, automation). Knowledge of model deployment or MLOps practices. Experience designing distributed systems or event-driven architectures. What We Offer Collaborative and supportive team environment. Opportunities to work with cutting-edge AI technologies. ...

Nice to Have Experience with containerization (Docker, Kubernetes). Exposure to DevOps tools (CI/CD pipelines, automation). Knowledge of model deployment or MLOps practices. Experience designing distributed systems or event-driven architectures. What We Offer Collaborative and supportive team environment. Opportunities to work with cutting-edge AI technologies. ...

every model we build is fair, free from bias, and explainable to consumers, customers, and regulators. Engineering & Deployment: Work within the engineering team on MLOps to containerise, deploy, and monitor models in high-scale production. Skills & Requirements Core Data Science: Foundational Stats: You must have an excellent grasp of Linear ...

every model we build is fair, free from bias, and explainable to consumers, customers, and regulators. Engineering & Deployment: Work within the engineering team on MLOps to containerise, deploy, and monitor models in high-scale production. Skills & Requirements Core Data Science: Foundational Stats: You must have an excellent grasp of Linear ...

engineers, business stakeholders) to define project scope, interpret model results, and clearly present data-driven recommendations to both technical and non-technical audiences. Deployment & MLOps: Collaborate with ML/Data Engineers to deploy, monitor, and maintain ML models in a production environment, ensuring stability and performance over time. Required Skills ...

engineers, business stakeholders) to define project scope, interpret model results, and clearly present data-driven recommendations to both technical and non-technical audiences. Deployment & MLOps: Collaborate with ML/Data Engineers to deploy, monitor, and maintain ML models in a production environment, ensuring stability and performance over time. Required Skills ...

testing, and statistical evaluation. Proven track record transitioning models from experimentation to production with governance and quality controls. Experience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, DVC). Experience developing LLM/GenAI applications, including prompt engineering and RAG architectures. Familiarity with LLMOps tooling (LangSmith, LangChain, LangGraph). ...

GitHub Actions. Knowledge of containerization (tools ie Docker) and orchestration (ie Kubernetes on Azure). Good awareness of Data & AI - understanding of ML lifecycle, MLOps, and Responsible AI. Strong problem-solving and analytical skills. Excellent communication and stakeholder management skills. Nice to have: Familiarity with LLM fine-tuning. Strong academic ...

Collaborate with software engineers to integrate models into live applications Monitor, evaluate, and improve model performance over time Contribute to best practice around MLOps, automation, and model governance Document solutions clearly for both technical and non-technical stakeholders Essential Skills and Experience Commercial experience as a Machine Learning Engineer ...

validation. Implement feature engineering and data augmentation techniques. Integration & Deployment Develop APIs or services to integrate ML models into production systems. Work with MLOps tools for model deployment, versioning, and monitoring. Performance Optimization Evaluate models using metrics like accuracy, precision, recall, F1-score. Optimize models for scalability, latency, and resource ...

applied AI Desirable Experience Hands-on experience with AWS services such as Lambda, ECS, S3, SageMaker or Bedrock Knowledge of ML engineering and MLOps practices (e.g. Docker, MLflow) Experience working in government, defence or other highly regulated environments Proven application of large language models (LLMs) to real-world problems Security ...

production-ready systems. Academic Foundation Bachelors or Masters degree in Computer Science, Machine Learning, Engineering , or a closely related discipline. Bonus Points Experience with MLOps and model serving platforms such as BentoML, MLflow, or SageMaker . Hands-on work with streaming and batch data pipelines using tools like Spark, Airflow ...

essential: Proficiency in Arcade scripting across ESRI runtime environments. Familiarity with the Microsoft Azure cloud platform, including data and AI services, and MLOps Knowledge of geospatial data warehousing and analytics. Familiarity with leading AI/ML platforms and libraries Experience building and scheduling ETL workflows. Knowledge of spatial data management ...

essential: Proficiency in Arcade scripting across ESRI runtime environments. Familiarity with the Microsoft Azure cloud platform, including data and AI services, and MLOps Knowledge of geospatial data warehousing and analytics. Familiarity with leading AI/ML platforms and libraries Experience building and scheduling ETL workflows. Knowledge of spatial data management ...

serving, versioning, and lifecycle management Lead documentation, knowledge transfer, and mentoring to upskill internal teams Technical requirements Strong Python and ML frameworks (PyTorch, TensorFlow) MLOps tooling (MLflow, Kubeflow or similar) Docker, Kubernetes, CI/CD pipelines Cloud platforms (AWS, Azure or GCP); Terraform experience preferred Financial services experience highly desirable ...

serving, versioning, and lifecycle management Lead documentation, knowledge transfer, and mentoring to upskill internal teams Technical requirements Strong Python and ML frameworks (PyTorch, TensorFlow) MLOps tooling (MLflow, Kubeflow or similar) Docker, Kubernetes, CI/CD pipelines Cloud platforms (AWS, Azure or GCP); Terraform experience preferred Financial services experience highly desirable ...

solutions using Python? Knowledge of frameworks such as PyTorch (preferred), TensorFlow, or Scikit-learn? Experience with NLP, deep learning, or computer vision? Familiarity with MLOps tools (Azure ML Pipelines, MLflow, Docker, Kubernetes)? Experience working with cloud platforms such as Azure, GCP, or AWS? The abbility to engage with technical ...

Data Science/ML roles Strong data science foundations (EDA, feature engineering, model evaluation, validation) Hands-on experience with ML models in production Cloud & MLOps exposure (GCP preferred; AWS/Azure acceptable) Experience with GenAI/LLMs (commercial or serious hands-on experimentation) Strong Python and modern ML tooling (e.g. ...

security clearance; sponsorship cannot be provided. Desirable: Knowledge of AWS services (e.g., Lambda, ECS, Bedrock, S3, SageMaker). Experience with ML engineering and MLOps (e.g., Docker, MLflow). Experience in regulated industries or government-related projects. Applying LLMs or advanced AI methods to real-world problems. Benefits: Competitive salary ...

your skills in a fast-moving AI environment. Nice to Have (But Not Essential) Experience with vector databases , embeddings, or prompt optimisation. Exposure to MLOps , model deployment, or monitoring tools. Background in data engineering, front-end development, or UX-aware AI applications . Interest or experience in healthcare or regulated ...

data engineering and contribute to architectural decisions Support senior team members in identifying and addressing data science opportunities. Required Skills & Experience Proven experience in MLOps or DevOps roles within machine learning environments Strong programming skills in Python, with hands-on experience in PySpark and SQL Deep understanding of ML lifecycle ...

These, But If Not Well Help You With Them Good knowledge of AWS cloud services (e.g. Lambda, ECS, Bedrock, S3, Sagemaker) ML Engineering and MLOps knowledge (e.g. Docker, ML flow) Experience working with government or other carefully regulated industries Track record of applying LLMs to robustly solve real problems Security ...