18 of 18 Permanent MLOps Jobs in the South West

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 ...

pipelines. Ability to analyse large datasets and communicate insights effectively. Experience with LLMs, generative AI, and prompt engineering. Knowledge of MLOps tools and frameworks (MLflow, Kubeflow, Airflow). Familiarity with SQL, NoSQL, and distributed data technologies. Experience deploying AI models in production environments. Publications, patents, or open-source contributions ...

experience. Commercial AI/ML development experience. Experience deploying machine learning solutions into operational environments. Strong collaboration and stakeholder engagement skills. Desirable SkillsExperience with MLOps, MLflow, Weights & Biases or similar experiment management platforms. Knowledge of cloud-based AI platforms such as AWS, Azure or GCP and containerisation technologies including Docker. ...

native data services and event-driven architectures Experience building data products that support analytics, reporting, machine learning, or AI use cases Knowledge of MLOps, AI Engineering, Agentic AI, or Generative AI implementation patterns Experience leading distributed engineering teams Experience establishing engineering standards, reusable frameworks, and community-of-practice initiatives Knowledge ...

analytics Delivering production agentic systems, including RAG pipelines, vector search, and agentic frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen, and n8n DevSecOps & DevOps MLOps Infrastructure as Code Building a truly diverse, equitable, and inclusive workplace is an ongoing journey, and were excited to keep learning and growing together. We have ...

already hold it even better. Desirable Experience with optical sensors. Camera optical frame processing. Knowledge of coordinate systems used in computer vision and robotics. MLOps experience. How to apply If this sounds like the right next step for you, apply now with your CV or get in touch with Laurence ...

/etc.) Genuine enthusiasm for AI-assisted development (LLMs/agents as core tools) Nice to have: regulated-environment experience, AI safety involvement, MLOps/CI/CD depth. If you think this role would be a good fit for you, please reach out to Charles Duran. ...

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 ...

ability to guide and coach the teamExcellent Python coding practice, including ML libraries such as HuggingFace, TensorFlow and PyTorchExperience building robust ML pipelines with MLOps tools for model training, monitoring and deploymentExperience with version control and environment management (e.g. venvs, Docker)Expert knowledge of AI/ML algorithms across different ...

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 ...

tested, and well-documented code suitable for long-term ownership and handover. Hands-on experience with production infrastructure, including Docker, Linux, CI/CD, MLOps, cloud platforms, and model serving architectures. Be able to work with state-of-the-art ML libraries to deliver powerful results quickly. Comfortable working ...

Management and Government business stakeholders across the full lifecycle, from hypothesis through to production deployment. Leveraging our AWS-based infrastructure you will apply modern MLOps/LLMOps tooling to run rigorous experiments, track results, and deliver scalable solutions. A key aspect to the role is to balance rapid experimentation with … methodology, and statistical evaluation Proven track record transitioning models from experimentation to production with appropriate governance and quality controls Experience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, Data Version Control) Experience developing LLM/GenAI applications, including prompt engineering and RAG architectures Familiarity with LLMOps tooling such ...

Evaluate emerging AI/ML developments, including Generative AI, and identify opportunities to apply them effectively. Own & Optimise: Contribute throughout the data science and MLOps lifecycle, continuously improving model performance and reliability. Engineer & Automate: Apply software engineering, version control and unit-testing practices to create maintainable production solutions. Cloud & Scale … sound software engineering and cloud skills. You will be comfortable taking AI/ML solutions beyond experimentation and into production, with knowledge of MLOps, AWS and modern deployment approaches. You should also maintain a strong awareness of developments across AI, including Generative AI, and be able to work collaboratively with ...

We are hiring an AI Engineer to design and build the embedded software, data pipelines and machine learning systems behind our sensing and communications technology. You will take ideas from a clean sheet through to ...

technologies that businesses can use to automate tasks and gain insights from their data. Key responsibilities include building complex models, designing and managing MLOps pipelines for CI/CD, monitoring, and model retraining. Mentoring junior members, influencing technical decisions within the team, and handling complex, non-routine problems. Our work … deploy complex machine learning models, ensuring high standards of quality, performance, and scalabilityDecide what model is most suitable for use in products and services MLOps Pipeline Management: Design and manage robust MLOps pipelines, including continuous integration/continuous delivery (CI/CD), monitoring, and model retraining, to ensure efficient ...

technologies that businesses can use to automate tasks and gain insights from their data. Key responsibilities include building complex models, designing and managing MLOps pipelines for CI/CD, monitoring, and model retraining. Mentoring junior members, influencing technical decisions within the team, and handling complex, non-routine problems. Our work … deploy complex machine learning models, ensuring high standards of quality, performance, and scalabilityDecide what model is most suitable for use in products and services MLOps Pipeline Management: Design and manage robust MLOps pipelines, including continuous integration/continuous delivery (CI/CD), monitoring, and model retraining, to ensure efficient ...

/agentic capabilities using the required scope. Use GCP Data Engineering to model, ingest, transform, secure, and serve data using the required scope. Use MLOps & Containerization to design, provision, configure, migrate, and tune platform components using the required scope. Use Telecom Domain Data & Architecture to implement domain-specific processes, rules …/agentic capabilities using the required scope. Use GCP Data Engineering to model, ingest, transform, secure, and serve data using the required scope. Use MLOps & Containerization to design, provision, configure, migrate, and tune platform components using the required scope. Use Telecom Domain Data & Architecture to implement domain-specific processes, rules ...

technologies that businesses can use to automate tasks and gain insights from their data. \n Key responsibilities include building complex models, designing and managing MLOps pipelines for CI/CD, monitoring, and model retraining. Mentoring junior members, influencing technical decisions within the team, and handling complex, non-routine problems. … complex machine learning models, ensuring high standards of quality, performance, and scalabilityDecide what model is most suitable for use in products and services \n MLOps Pipeline Management: Design and manage robust MLOps pipelines, including continuous integration/continuous delivery (CI/CD), monitoring, and model retraining, to ensure efficient ...