101 to 125 of 702 MLOps Jobs in the UK

expertise on AWS, especially Amazon Bedrock and Bedrock AgentCore, alongside SageMaker, Lambda and Step Functions, with CDK or equivalent IaC. Hands-on LLMOps and MLOps experience, including agent and trajectory evaluation, tracing (e.g. OpenTelemetry), guardrails, cost monitoring and automated model pipelines. A clear understanding of agent security risks, such ...

Cloud DevOps Limited is seeking an AI Engineer in Cardiff to design, build and deploy production-grade AI solutions, including generative AI apps and RAG systems on AWS and Azure. You will work with cloud ...

frameworks. Azure AI Foundry, Azure OpenAI, Azure Machine Learning, Databricks, or equivalent cloud platforms. Data visualisation and storytelling tools such as Power BI. MLOps and model lifecycle management. Preferred Qualifications Experience in client-facing consulting roles. Exposure to cloud-based AI and analytics platforms. Understanding of Responsible AI and governance ...

behaviours. Write production-quality Python and SQL, and work with engineers to integrate successful solutions into scalable AWS services, data pipelines, and APIs. Apply MLOps and LLMOps practices, including version control, automated tests, CI/CD, reproducible deployment, monitoring, drift detection, and rollback. Define and maintain offline and live evaluation ...

curiosity to learn others. Bridging Data & AI: Understanding how AI fits into the broader data landscape, interest in integrating LLMs and AI/MLOps into client solutions. Collaborative Leadership: Proven track record of supporting multi-squad deliveries, guiding stakeholders through architectural trade-offs, and contributing to technical proposals. Our Benefits ...

domains. Ability to lead architectural discussions and technical trade‐offs without losing sight of the details. Familiarity with cloud infrastructure (AWS preferred) and modern MLOps practices. Commitment to people development: mentoring, performance conversations, and building high‐performing teams. Bonus: experience with NLP on financial contracts, RAG or LLM‐based applications ...

Skills: Hands-on experience with data lineage tools and techniques, including graph & vector databases and metadata management platforms Hands-on experience with LLM Ops, MLOps, and AI/ML platform deployment at scale Familiarity with business-led analytics delivery models and rapid prototyping frameworks Experience with AI/ML governance ...

across fulfilment ranking, model delivery, offline and online evaluation, low-latency services, observability and product quality. Its published work includes shared model-serving and MLOps paths, automated evaluation and retraining, caching and telemetry, and agent-assisted release and operational workflows. What is the role? We are looking for a Senior ...

behaviours. Write production-quality Python and SQL, and work with engineers to integrate successful solutions into scalable AWS services, data pipelines, and APIs. Apply MLOps and LLMOps practices, including version control, automated tests, CI/CD, reproducible deployment, monitoring, drift detection, and rollback. Define and maintain offline and live evaluation ...

services such as Azure OpenAI, AI Foundry or AI Search, with Docker and Kubernetes Understanding of schema validation frameworks (e.g., JSON Schema, Pydantic) and MLOps tools such as MLflow or Airflow Strong communication ability to explain trade-offs around cost, latency and accuracy to technical and non-technical audiences Nice ...

maintain real-time AI pipelines using WebSockets and SSE , ensuring seamless low-latency delivery for voice (ASR/TTS) and text applications. Architecture & MLOps: Deploy and orchestrate models within containerized microservice architectures ( Docker/Kubernetes ), ensuring robust monitoring, security, and scalability. Collaborative Delivery: Work closely with Business Analysts and internal ...

design principles. Technical Stack Core: Power BI SQL Server Tool-agnostic architecture patterns Cloud platform comparison (Azure, AWS, GCP), API design Microsoft Fabric MLOps frameworks What You'll Bring Professional experience spanning multiple areas of data and AI (analytics, engineering, or data science). Strong understanding of the complete data ...

with measurable impact, including deploying or working alongside ML/AI systems in production Bonus: Trust & Safety, Content Moderation, or Integrity engineering experience MLOps experience: deployment, monitoring, and versioning of ML models Experience with SQL, data analysis tools, real‐time streaming systems (Kafka, Redis), or event‐driven architectures Familiarity with ...

knowledge in the following: Python AWS Kubernetes Spark & distributed computing dbt Terraform Data Warehousing (we use Databricks but experience with other platforms is fine) MLOps implementation (desirable but not required) Airflow (or other orchestration tooling) Governance Things to note Just to let you know, we are dog friendly company ...

along with data extraction, manipulation and feature engineering techniques Experience with Spark, agile delivery methods and CI/CD practices Familiarity with DevOps and MLOps tools and practices, such as Docker, Terraform and MLFlow Confidence influencing and communicating with stakeholders across technical and non-technical teams Ideally, exposure to cloud ...

Learning Applied machine learning, computer vision, object detection, image classification, model development and evaluation, data annotation and curation, training pipelines, performance optimisation, edge ML, MLOps, deployment and maintenance of ML solutions, Python development, Python‐based ML frameworks, PyTorch, TensorFlow, sklearn, OpenCV, NumPy, feature development, code review, pytest, linting, bug fixing ...

optimize visualization performance Enhance dash boarding capabilities to deliver fast and flexible experiences Tech stack: React, AngularJS, Angular, D3.js, ECharts, Node.js, Java, Spring MLOps: Develop back-end capabilities for automating and monitoring ML model lifecycles while supporting cross-functional collaboration. Build tools to automate retraining, monitoring, and deployment ...

datasets (scientific, biomedical, or enterprise data) Familiarity with scientific ontologies and metadata standards (e.g., MeSH, UMLS, ORCID, CrossRef) Exposure to production ML systems and MLOps practices Familiarity with data visualization and analytical tooling (e.g., Tableau, Power BI, matplotlib, seaborn, or similar) to communicate insights effectively Experience with human ...

such as scikit-learn, XGBoost, TensorFlow, PyTorch or Keras Solid grounding in modern software engineering, infrastructure and data tooling, and an understanding of the MLOps challenges across the full ML lifecycle. You don’t need to have built a platform, but you know what one has to handle Familiarity with ...

such as scikit-learn, XGBoost, TensorFlow, PyTorch or Keras Solid grounding in modern software engineering, infrastructure and data tooling, and an understanding of the MLOps challenges across the full ML lifecycle. You don’t need to have built a platform, but you know what one has to handle Familiarity with ...

documenting and defending architectural decisions and engaging in thoughtful technical debate. Not required, but it’s a plus if you also have: Experience with MLOps/LLMOps frameworks and best practices A PhD in Computer Science, Electrical Engineering or a related field. A background or work experience in life‐sciences ...

production ML or data engineering experience considered. 8+ years of hands‐on experience building, deploying, and operating production ML systems on Azure, with practical MLOps and CI/CD experience including experiment tracking, deployment hygiene, monitoring, evaluation, and operational documentation. Strong Python and software engineering fundamentals, including testing, version control ...

Azure) and their data services. Tool & Product Development: Hands-on experience in developing and maintaining analytical tools, dashboards, and automated solutions. Familiarity with MLOps principles and practices for productising data science models. AI & Generative AI: Proven ability to identify and implement AI-driven solutions to enhance analytical capabilities and efficiency. ...

preparation, feature engineering, and feature stores, and understand why reproducibility and point-in-time correctness matter more than they first appear Have worked with MLOps in practice rather than in principle: model versioning and registries, experiment tracking, automated retraining, and monitoring for drift and model degradation (MLflow, SageMaker, Databricks ...

preparation, feature engineering, and feature stores, and understand why reproducibility and point-in-time correctness matter more than they first appear Have worked with MLOps in practice rather than in principle: model versioning and registries, experiment tracking, automated retraining, and monitoring for drift and model degradation (MLflow, SageMaker, Databricks ...