226 to 250 of 1,173 MLOps Jobs in the UK

PREFERRED QUALIFICATIONS: Meteorological understanding/experience with weather modelling Prior knowledge or experience in the power markets or energy sector. Experience with cloud platforms (e.g., AWS, GCP, Azure) and MLOps practices. Familiarity with data visualization tools (e.g., Tableau, Power BI). COMPENSATION & BENEFITS: Competitive salary + bonus. Hybrid working arrangement (minimum 3 days in the London office) #J-18808-Ljbffr More ❯

services use cases, such as credit scoring, fraud detection, customer segmentation and predictive modeling Lead the process of taking AI/ML models from development to production, ensuring robust MLOps practices tailored to financial data environments Monitor and manage model performance, including addressing issues related to explainability, data drift and model drift in financial models Collaborate with risk, compliance and More ❯

services use cases, such as credit scoring, fraud detection, customer segmentation and predictive modeling Lead the process of taking AI/ML models from development to production, ensuring robust MLOps practices tailored to financial data environments Monitor and manage model performance, including addressing issues related to explainability, data drift and model drift in financial models Collaborate with risk, compliance and More ❯

services use cases, such as credit scoring, fraud detection, customer segmentation and predictive modeling Lead the process of taking AI/ML models from development to production, ensuring robust MLOps practices tailored to financial data environments Monitor and manage model performance, including addressing issues related to explainability, data drift and model drift in financial models Collaborate with risk, compliance and More ❯

services use cases, such as credit scoring, fraud detection, customer segmentation and predictive modeling Lead the process of taking AI/ML models from development to production, ensuring robust MLOps practices tailored to financial data environments Monitor and manage model performance, including addressing issues related to explainability, data drift and model drift in financial models Collaborate with risk, compliance and More ❯

leveraging cloud-native tools and services. AI Feature Integration Collaborate with engineering and product teams to embed AI-driven functionality into user-facing software. Technical Leadership Set standards for MLOps, automation, and performance tuning across the engineering team. Generative AI & LLMs Explore and integrate modern techniques such as large language models and generative architectures. Support peers through code reviews, design More ❯

models and AI features into broader software systems. • Collaborate closely with product managers, data scientists, and other engineers to define requirements, design solutions, and deliver high-quality features. • Implement MLOps practices for model versioning, deployment, monitoring, and lifecycle management. • Write clean, maintainable, well-documented, and tested code following software engineering best practices (e.g., SOLID principles, CI/CD). • Stay More ❯

like Apache Airflow, Spark, or 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 mindset and ability to work More ❯

working with unstructured data and NLP-related datasets. Proficiency in one programming language, preferably Python with experience in data processing libraries such as Pandas, PySpark, or Dask. Familiarity with MLOps and deploying AI/ML models into production environments. Knowledge of Retrieval-Augmented Generation (RAG) frameworks or interest in learning and supporting RAG systems. Experience implementing scalable APIs and integrating More ❯

systems design, with the ability to architect and explain data pipelines, ML pipelines, and ML training and serving approaches. Experience with CI/CD solutions in the context of MLOps and LLMOps including automation with Infrastructure as Code (IaC) solutions (e.g. Ansible). Experience delivering technical presentations and leading business value sessions. Executive presence with public speaking skills. Preferred Qualifications More ❯

domains Expertise in model validation, explainability, governance and ethical AI principles Advanced proficiency in Python (e.g. Scikit-learn, TensorFlow/PyTorch) and SQL, plus familiarity with ML engineering and MLOps practices Nice to have: Experience in setting or influencing data science strategy in a growing organisation Familiarity with AWS SageMaker and modern cloud-based ML workflows History of publications, open More ❯

of the development lifecycle from idea generation to testing, implementation and ensuring business value across different business areas and markets. With the support of more senior data scientists and MLOps engineers, you will ensure that solutions are rigorously tested, deliver business value and are then optimised over their lifecycle. We expect solutions to make use of efficient algorithms whilst enabling More ❯

including deep learning applications; C/C++ for computer vision, geometry processing, or scientific computing; software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps); container-ization and orchestration (Docker, Kubernetes, Slurm); writing pipelines and experiment environments, including running experiments in pipelines in a systematic way. What we offer Be part of something larger: Make More ❯

clearly communicate and present to internal and external stakeholders. Nice to have, but not essential NLP/Deep learning experience (e.g. huggingface, spaCy) Deep learning framework experience (preferably PyTorch) MLOps experience (e.g. data and model versioning, model deployment CI/CD, MLFlow/DVC) Cloud platform experience, especially from an ML standpoint (AWS preferred) Statistical testing experience Experience with AWS More ❯

and fine-tuning Large Language Models Proficient in vector databases (Pinecone, Weaviate, Milvus) and embedding technologies Expert in building RAG (Retrieval-Augmented Generation) systems at scale Strong experience with MLOps practices and model deployment pipelines Proficient in cloud AI services (AWS SageMaker/Bedrock) Deep understanding of distributed systems and microservices architecture Expert in data pipeline platforms (Apache Kafka, Airflow More ❯

and fine-tuning Large Language Models Proficient in vector databases (Pinecone, Weaviate, Milvus) and embedding technologies Expert in building RAG (Retrieval-Augmented Generation) systems at scale Strong experience with MLOps practices and model deployment pipelines Proficient in cloud AI services (AWS SageMaker/Bedrock) Deep understanding of distributed systems and microservices architecture Expert in data pipeline platforms (Apache Kafka, Airflow More ❯

clearly communicate and present to internal and external stakeholders. Nice to have, but not essential NLP/Deep learning experience (e.g. huggingface, spaCy) Deep learning framework experience (preferably PyTorch) MLOps experience (e.g. data and model versioning, model deployment CI/CD, MLFlow/DVC) Some basic devops experience useful (kubectl, helm, etc.) Cloud platform experience, especially from an ML standpoint More ❯

e.g., Kafka, Spark Streaming). Proficiency in CI/CD pipelines for data deployment using Azure DevOps, GitHub Actions, or Terraform for Infrastructure as Code (IaC). Understanding of MLOps principles, including continuous integration (CI), continuous delivery (CD), and continuous training (CT) for machine learning models. Experience with performance tuning and query optimisation for efficient data workflows. Strong understanding of More ❯

with cloud platforms like Microsoft Azure or Google Cloud Platform (GCP); familiarity with Databricks is a plus. Comfort working in cloud-based or secure environments, with an interest in MLOps or model deployment. Awareness and practice of responsible AI, data ethics, and governance frameworks. Willingness to explore emerging technologies such as agentic AI, GitHub Copilot, Microsoft Copilot and Copilot Studio. More ❯

with cloud platforms like Microsoft Azure or Google Cloud Platform (GCP); familiarity with Databricks is a plus. Comfort working in cloud-based or secure environments, with an interest in MLOps or model deployment. Awareness and practice of responsible AI, data ethics, and governance frameworks. Willingness to explore emerging technologies such as agentic AI, GitHub Copilot, Microsoft Copilot and Copilot Studio. More ❯

will also oversee traditional deep learning model development from design, training, evaluating to deployment, with a focusing on fine turn LLMs. The role will interact with product, design, engineering, MLops, and domain experts and partners. When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives More ❯

s no shortage of learning opportunities at DATAPAO, meaning that you'll get access to Databricks' public and internal courses to learn all the tricks of Distributed Data Processing, MLOps, Apache Spark, Databricks, and Cloud Migration from the best. Additionally, we'll pay for various data & cloud certifications, you'll get dedicated time for learning during work hours, and access More ❯

large organizations. You have experience developing state-of-the-art deep learning models, LLMs, and advanced AI architectures. You are an industry expert in ML model development, deployment, and MLOps at scale. You are deeply comfortable with Python, SQL, and ML frameworks (e.g., TensorFlow, PyTorch) and have experience working in a microservices architecture (Go Lang experience is a plus). More ❯

s no shortage of learning opportunities at DATAPAO, meaning that you'll get access to Databricks' public and internal courses to learn all the tricks of Distributed Data Processing, MLOps, Apache Spark, Databricks, and Cloud Migration from the best. Additionally, we'll pay for various data & cloud certifications, you'll get dedicated time for learning during work hours, and access More ❯

GenAI. Strong understanding of data ecosystem components including data lakes, data warehouses, data mesh architectures, and associated architectural patterns. Strong understanding of methodologies and principles of data science and MLOps, including model development, validation, and lifecycle management. Experience in data pipeline design, ETL/ELT processes, and data integration approaches and appreciation of trade-offs associated with specific use-cases More ❯