151 to 175 of 1,319 MLOps Jobs in the UK

on experience with cloud platforms (e.g., AWS, GCP, Azure) and deploying AI models in production environments. Solid understanding of data structures, algorithms, and software engineering best practices. Experience with MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes) is highly desirable. Excellent problem-solving, communication, and collaboration skills. #J-18808-Ljbffr More ❯

Points Additional skills that would be beneficial include: Experience in a startup environment Knowledge of Python frameworks (Django, Flask, FastAPI), Rust, and machine learning libraries Familiarity with Generative AI, MLOps, CI/CD, and cloud platforms (AWS/GCP/Azure) Expertise with container tools like Docker and Kubernetes Ready to take the next step in your engineering career? Apply More ❯

TensorFlow, PyTorch, pyMC, pgmpy, ...) Hands-on experience with one or more cloud computing platforms (Azure - preferred, AWS, GCP). Understanding of the whole ML lifecycle and experience with MLOps/DataOps. Experience with Probabilistic Graphical Modelling (Bayesian Networks, Markov Random Fields, Factor Graphs, ...) Strong problem-solving skills and attention to detail. Good communication skills, fluent English. If interested More ❯

. Data quality techniques. Data normalisation. Cloud and Big Data Technologies. Extensive experience with Databricks. Familiarity with cloud data warehouses (AWS, Azure, GCP, or Snowflake). Additional Desirable Skills MLOps experience advantageous. Knowledge of advanced machine learning algorithms and statistics. Experience in Natural Language Processing. CI/CD knowledge. Experience with JIRA/Asana for project management. Familiarity with Airflow More ❯

to extract insights and communicate results effectively. Innovate new modelling and machine learning approaches to enhance existing processes and capabilities. Collaborate with cross-functional teams, including engineering, product management, MLOps, and leadership, to define problem statements, scope projects, and deliver analytics solutions. Mentor peers in data science and model development, promoting best practices. Remain abreast of the latest technologies and More ❯

to extract insights and communicate results effectively. Innovate new modelling and machine learning approaches to enhance existing processes and capabilities. Collaborate with cross-functional teams, including engineering, product management, MLOps, and leadership, to define problem statements, scope projects, and deliver analytics solutions. Mentor peers in data science and model development, promoting best practices. Remain abreast of the latest technologies and More ❯

60k per annum As a Machine Learning Engineer , you will be part of an MLOps team, working alongside data scientists, software engineers, and other stakeholders to bring machine learning models to life. You will be responsible for deploying, maintaining, and monitoring models in production, improving model performance, and refining the machine learning infrastructure to support business objectives. Your role will More ❯

/ML workloads on GCP (e.g., BigQuery, Dataflow). • Solid understanding of core AI/ML concepts, including deep learning, neural networks, NLP, and machine learning algorithms. • Familiarity with MLOps principles and tools for deploying, managing, and monitoring ML models in production. • Excellent problem-solving skills, with a passion for building innovative and impactful AI solutions. • Google Professional Cloud Architect More ❯

/ML workloads on GCP (e.g., BigQuery, Dataflow). • Solid understanding of core AI/ML concepts, including deep learning, neural networks, NLP, and machine learning algorithms. • Familiarity with MLOps principles and tools for deploying, managing, and monitoring ML models in production. • Excellent problem-solving skills, with a passion for building innovative and impactful AI solutions. • Google Professional Cloud Architect More ❯

Not Essential): Familiarity with deep learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits: 💰 Competitive Salary & Bonus : £35,000 – £45,000 plus performance-based incentives 🏡 Hybrid Working : A flexible mix of office and remote work 📈 Career More ❯

Not Essential): Familiarity with deep learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits: 💰 Competitive Salary & Bonus : £35,000 – £45,000 plus performance-based incentives 🏡 Hybrid Working : A flexible mix of office and remote work 📈 Career More ❯

Not Essential): Familiarity with deep learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits: Competitive Salary & Bonus : £35,000 – £45,000 plus performance-based incentives Hybrid Working : A flexible mix of office and remote work Career More ❯

5+ years of experience in technical product management Experience working with developer teams and productionizing AI or other data-intensive applications Breadth of knowledge or familiarity with GenAI platforms, MLOps platforms, AI toolkits Deep understanding of software development best practices Organizational and communication skills to effectively coordinate and work with engineers, UX, subject matter experts, other product managers, and senior More ❯

/ML workloads on GCP (e.g., BigQuery, Dataflow). • Solid understanding of core AI/ML concepts, including deep learning, neural networks, NLP, and machine learning algorithms. • Familiarity with MLOps principles and tools for deploying, managing, and monitoring ML models in production. • Excellent problem-solving skills, with a passion for building innovative and impactful AI solutions. • Google Professional Cloud Architect More ❯

a technical team and collaborate with stakeholders. A passion for lean Agile software engineering. Whilst not necessary to apply, we’d love you to also have: Experience of AI & MLOPs pipelines. Experience of working with simulation environments and the use of simulation protocols such as DIS and HLA. Experience of network programming and protocols such as HTTP(s), SSH, TCP More ❯

of machine learning algorithms, statistics, and predictive modeling Proficiency with Python and machine learning toolkits like pandas, scikit-learn, and optionally pytorch/tensorflow. Experience with machine learning operations (MLOps) and productionization of ML models. Familiarity with building data and metric generation pipelines, using tools like SQL or Spark, to answer business questions and assess system efficacy. Ability to communicate More ❯

coupled with a deep understanding of data modelling principles. Mastery of machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and their application in complex problem domains. Extensive experience with MLOps principles and tools for model deployment, monitoring, versioning, and lifecycle management. Proven experience with analytics and visualisation toolkits such as Qlik, Looker, or similar platforms, along with basic API skills More ❯

coupled with a deep understanding of data modelling principles • Mastery of machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and their application in complex problem domains • Extensive experience with MLOps principles and tools for model deployment, monitoring, versioning, and lifecycle management • Proven experience with analytics and visualisation toolkits such as Qlik, Looker, or similar platforms along with Basic API skills More ❯

to embed intelligent features into our platform. Ensure smooth end-to-end integration of AI/ML functionality within broader product development efforts. Technical Leadership Define best practices for MLOps, model deployment, and performance tuning. Evaluate and implement new frameworks, tools, and cloud-native AI solutions (including LLMs). Make strategic technical decisions that align with broader business goals. Support More ❯

as neural networks, support vector machines (SVM), tree-based methods, and deep learning algorithms. Knowledge of natural language understanding and generative AI is essential. The role requires proficiency in MLOps practices, especially in deploying production applications in cloud environments like AWS or Azure. Experience with generative AI tools, prompt engineering, model fine-tuning, and selecting appropriate models for specific tasks More ❯

to embed intelligent features into our platform. Ensure smooth end-to-end integration of AI/ML functionality within broader product development efforts. Technical Leadership Define best practices for MLOps, model deployment, and performance tuning. Evaluate and implement new frameworks, tools, and cloud-native AI solutions (including LLMs). Make strategic technical decisions that align with broader business goals. Support More ❯

to embed intelligent features into our platform. Ensure smooth end-to-end integration of AI/ML functionality within broader product development efforts. Technical Leadership Define best practices for MLOps, model deployment, and performance tuning. Evaluate and implement new frameworks, tools, and cloud-native AI solutions (including LLMs). Make strategic technical decisions that align with broader business goals. Support More ❯

to embed intelligent features into our platform. Ensure smooth end-to-end integration of AI/ML functionality within broader product development efforts. Technical Leadership Define best practices for MLOps, model deployment, and performance tuning. Evaluate and implement new frameworks, tools, and cloud-native AI solutions (including LLMs). Make strategic technical decisions that align with broader business goals. Support More ❯

to embed intelligent features into our platform. Ensure smooth end-to-end integration of AI/ML functionality within broader product development efforts. Technical Leadership Define best practices for MLOps, model deployment, and performance tuning. Evaluate and implement new frameworks, tools, and cloud-native AI solutions (including LLMs). Make strategic technical decisions that align with broader business goals. Support More ❯

to embed intelligent features into our platform. Ensure smooth end-to-end integration of AI/ML functionality within broader product development efforts. Technical Leadership Define best practices for MLOps, model deployment, and performance tuning. Evaluate and implement new frameworks, tools, and cloud-native AI solutions (including LLMs). Make strategic technical decisions that align with broader business goals. Support More ❯