18 of 18 Permanent MLOps Jobs in the South West

.Experience building and deploying Generative AI solutions, including prompt engineering, fine-tuning, and evaluation of large language models (LLMs) or other generative architectures.Understanding of MLOps principles, including automated model training, CI/CD for ML, model versioning, monitoring, and retraining.Experience deploying AI and GenAI solutions on cloud platforms (AWS, Azure ...

Relevant cloud, AI, or security certifications Experience deploying ML models into production environments Experience with Kubernetes, OpenShift, or cloud-native deployment architectures Experience with MLOps tooling (e.g. MLflow, Kubeflow, model monitoring platforms) Experience applying DevSecOps practices within cloud or controlled environments Exposure to large language models or generative AI systems ...

equivalent platforms.Developing high-quality, maintainable code and working with software engineering practices such as version control, testing, code review and CI/CD.Working with MLOps or workflow tools such as MLflow, Kubeflow, Argo or equivalent frameworks.Scaling models or analytical services for enterprise or operational use.Applying statistical methods, model evaluation techniques ...

generative AI and the modern AI stack, including large language models, prompt engineering, retrieval-augmented generation (RAG), vector databases, evaluation/monitoring and MLOps principles, with the ability to shape investment cases and operating models at scale.Extensive experience providing executive oversight for production AI delivery (you steer architecture and engineering ...

customers and internal stakeholders.Excellent coding practice using Python and associated ML packages (HuggingFace, TensorFlow, PyTorch), following best practices.Experience developing robust ML pipelines with MLOps tools for model training, monitoring and deployment.Experience of appropriate version control and environment management (such as venvs or Docker).Experience of writing technical project proposals.Experience ...

working with cloud-based machine learning and data platforms, including technologies such as Vertex AI, BigQuery, Cloud Run or equivalent cloud services.Practical experience implementing MLOps tooling and frameworks such as Kubeflow Pipelines, dbt or similar solutions, including monitoring and operationalising large-scale models.Experience working with Infrastructure as Code, Kubernetes ...

agile delivery environmentStrong problem‐solving skills and attention to detailDesired Skills:Experience with cloud‐based ML services (AWS SageMaker, Azure ML, Databricks)Familiarity with MLOps practices and model lifecycle managementExperience with NLP, deep learning, or computer visionKnowledge of data visualisation tools (Power BI, Tableau)Exposure to big‐data technologies (Spark ...

development and deployment of ML and AI models to prove and deliver tangible commercial value. Act as a technical bridge between Data Science and MLOps to embed best practice deployment techniques across the team. Take hands-on ownership of big projects, working with a large, event-based data set. Research ...

areas, and encourage anyone with the experience listed above to apply. We would particularly like to encourage applicants with experience in the following areas: MLOps: including tools like LangChain for application development and managing the ML lifecycle Data Engineering: including platforms like Databricks for data processing and analytics DevSecOps & DevOps ...

agreed requirements, delivery plans and quality standards.Support the deployment and operationalisation of AI systems, including model monitoring, performance optimisation, CI/CD and MLOps practices.Collaborate with solution architects, data scientists, business analysts and engineers to ensure AI solutions are integrated effectively into wider technology and business environments.Apply responsible AI, security … equivalent. Practical knowledge of Generative AI techniques, including large language models, prompt engineering, retrieval-augmented generation, model evaluation and responsible AI considerations. Understanding of MLOps and production AI practices, including CI/CD, model versioning, monitoring, automated testing, retraining and performance optimisation. Experience using cloud platforms and cloud-native ...

Data Science team, working on high-impact projects across national security and defence. Youll design, build and deploy production-grade software, infrastructure and MLOps systems that use machine learning to solve complex real-world problems. The Role Build scalable software and infrastructure for ML systems. Deploy and operationalise machine learning ...

including machine learning models, data pipelines and application components that are secure, reliable and performant.Drive the operationalisation of AI solutions using modern engineering and MLOps practices, including CI/CD, model monitoring, testing and lifecycle management.Ensure solutions are developed in line with security, privacy, governance and responsible AI requirements, particularly … practical implementation of model development and deployment.Working knowledge of Generative AI, including large language models, prompt engineering, retrieval-augmented generation and evaluation approaches.Experience with MLOps and modern engineering practices, including CI/CD pipelines, model versioning, monitoring and automated testing.Experience deploying AI solutions on cloud platforms such as Azure ...

Engineering: Deep prompt engineering & optimization skills for GenAI.Vector DBs & RAG: Designed solutions using vector DBs & Retrieval Augmented GenAI (RAG) for knowledge applications.GenAI ModelOps/MLOps & Governance: GenAI ModelOps/MLOps knowledge with ethical AI governance focus.Multimodal & Code Generation: Architected GenAI for multimodal (vision, audio, language) & code generation.ER&I GenAI Applications ...

test and deploy machine learning models, analytical pipelines and data science components that are robust, scalable and maintainable.Support the operationalisation of ML solutions through MLOps practices, including model monitoring, versioning, automated testing, CI/CD and performance optimisation.Apply responsible AI, model explainability, security, privacy and governance considerations throughout the development … PyTorch, TensorFlow or equivalent.Experience across the end-to-end ML lifecycle, including data preparation, feature engineering, model development, validation, deployment, monitoring and optimisation.Understanding of MLOps and production ML practices, including CI/CD, model versioning, automated testing, retraining, monitoring and reproducible delivery.Experience using cloud platforms and data science environments such ...

leaders. You are aligned and accountable.You are comfortable with ambiguity, fast cadence, and ownership. Excuses do not ship parts. Execution does.You have MLOps and production deployment experience at scale.Great plus:Background in composites, advanced manufacturing, aerospace, automotive, or defense.Experience with computer vision applied to automated inspection, generative design, reinforcement learning ...

data solutions. This role will see you building and maintaining scalable platform capabilities, troubleshooting existing services, and providing technical support to Data Scientists and MLOps teams working across a range of technologies. Key Responsibilities of the Analytics Platform Engineer Design, build and maintain scalable analytics platforms and cloud-based infrastructure. … platform capabilities while enhancing and maintaining existing analytics solutions. Troubleshoot technical issues, implement improvements and optimise platform performance. Collaborate closely with Data Scientists, MLOps Engineers and other technical teams to deliver reliable, secure and high-performing solutions. Adapt to a varied workload, solving complex engineering challenges across a range ...

will develop, test, deploy, and enhance platform capabilities, resolve issues within existing analytics services, and provide hands-on engineering support to Data Scientists and MLOps teams working across a range of technologies. You will operate in a dynamic environment with varied and sometimes unfamiliar technical challenges, requiring strong problem-solving … lifecycle. Design, develop, test, and deployment of a new analytic hosting platform. Bugfixes and feature requests for existing mission facing analytics. Engineering support to MLOps engineers and Data Scientists. These are unpredictable and highly variable requirements, potentially using unfamiliar technology, so a flexible style is required. In this role ...

cloud migration, analytics platforms, and data-driven solutions. This is an excellent opportunity to work on mission-critical systems, supporting Data Scientists and MLOps teams while helping to design, build and evolve a scalable analytics hosting platform. Start Date: ASAP Job Type: Contract or Permanent Duration: 2 Months Initially … across the full software development life cycle, delivering platform enhancements, resolving issues within existing analytics services, and providing engineering support to Data Scientists and MLOps teams. Key Responsibilities: Designing, developing, testing and deploying a new analytics hosting platform Supporting cloud migration and data management initiatives Building scalable, secure, and resilient ...