1 to 25 of 49 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 ...

PyTorch, or Java-based ML libraries) Strong problem-solving and communication skills Preferred Qualifications Experience with NLP, computer vision, or recommendation systems Familiarity with MLOps practices and model lifecycle management Knowledge of event-driven architectures (Kafka or similar) Experience with DevOps tooling and infrastructure as code Exposure to Generative ...

Intelligence, Data Science, or a related technology discipline Minimum 2:1 degree classification (or equivalent) Desirable Azure OpenAI, AWS Bedrock and Google Gemini experience MLOps, Data Engineering or Platform Engineering experience Experience leading technical teams Knowledge of Agentic AI, AI Agents and emerging AI frameworks What's On Offer? Opportunity ...

model evaluation, responsible AI controls and enterprise integration patterns.Data and AI platform architecture on AWS, including data ingestion, governance, feature management, model lifecycle management, MLOps, observability and secure deployment of AI workloads.Container technologies such as Docker and associated orchestration platforms including Amazon EKS, Kubernetes, Amazon ECS and OpenShift.Infrastructure as code ...

evaluation, responsible AI controls and enterprise integration patterns.· Data and AI platform architecture on AWS, including data ingestion, governance, feature management, model lifecycle management, MLOps, observability and secure deployment of AI workloads.· Container technologies such as Docker and associated orchestration platforms including Amazon EKS, Kubernetes, Amazon ECS and OpenShift.· Infrastructure ...

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 ...

Familiarity with Data Mesh or Lakehouse architectures.Familiarity with Kubernetes, Docker, and real-time streaming technologies (e.g. Kafka, Kinesis).Exposure to ML engineering pipelines or MLOps frameworks.What’s it like to work at Zego At Zego, we operate like a high-performing sports team. We come together as one, driven ...

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 ...

Computer Science, Data Science, Machine Learning, or related field. Proven experience building and deploying AI applications in large-scale production environments. Experience with MLOps practices and tools for managing the machine learning lifecycle. Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, and scikit-learn. Strong understanding ...

Retrieval-Augmented Generation (RAG) and agentic AI solutions, including evaluation, optimisation and monitoring.Implement reliable and scalable AI solutions using established software engineering, MLOps and LLMOps practices.Contribute across the AI delivery lifecycle from experimentation and prototyping through to production deployment and operational support.Collaborate with platform engineering teams to integrate AI capabilities ...

Data Engineering - Python, SQL, ETL/ELT, modern cloud data platforms, Spark/Databricks and data architecture AI & Machine Learning - ML Engineering, MLOps, Python, model deployment, evaluation, LLM/GenAI and production AI Technical Leadership - Senior/Lead engineering roles across Cloud, Platform, Data and AI The organisations vary ...

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 ...

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 ...

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 ...

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 Why else you'll love it here Wondering what the salary for this role ...

Develop and implement machine learning algorithms, models, and computational systems; design and build scalable data pipelines to support model training and production with DevOps &MLOps Customize and apply Deep Learningand Gen AI models for various use cases based on the business needs, data availability,systemand infrastructure requirements - including edge device ...

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 ...

performance and cost-effectiveness. AI: Develop and deploy AI/ML models into production environments. Build scalable inference pipelines and AI APIs. Implement MLOps workflows for model versioning, monitoring, and retraining. Collaborate with data scientists to productionize machine learning solutions. Evaluate and optimize AI model performance and operational efficiency. Database ...

obligations. Design scalable, secure, and resilient data platforms, including cloud, hybrid, and on‐premise architectures. Lead architecture for advanced analytics, machine learning, GenAI, and MLOps, ensuring production‐grade robustness. Establish AI governance, data management, and responsible AI frameworks, including security, privacy, explainability, and model risk controls. Translate business outcomes into ...

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 ...

skills Experience with Linux operating systems (e.g. Red Hat, Ubuntu) Experience working within a variety of development frameworks and practices e.g. DevOps, DevSecOps, SCRUM, MLOps, XP. Experience with data analysis and manipulation tools (e.g. Pandas) Experience of a broad section of the Software Development Lifecycle (SDLC) with specific focus ...

Event Hubs, or Kinesis for real-time data processing Knowledge of data architectures supporting AI/ML workloads, including vector databases, feature stores and MLOps pipelines Experience processing and managing unstructured data types (text, images, logs, sensor data) Understanding of Lambda and Kappa architectural patterns and when to apply each ...

Knowledge of C++, Python, PyTorch, OpenCV, CUDA, TensorRT, Git, WSL, Docker, Jira/Confluence Machine Learning and Deep Learning Computer Vision methodologies and algorithms MLOps Developing AI for use on Edge Computers Research and prototyping Benefits: Annual Company Bonus – Based on company performance 25 Days holiday not including bank holidays ...

Experience using Python and R to develop and deploy models in cloud environments (Azure experience is advantageous). Understanding of machine learning lifecycles and MLOps principles. Experience working with databases, APIs and collaborative development tools. Sharp problem-solving and analytical skills, with attention to detail and sound judgement. Confident communication ...