26 to 33 of 33 MLOps Jobs in Glasgow

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 ...

fine‐tuning foundation models (LoRA/QLoRA), and deploying multi‐agent autonomous systems that have run in production not just proofs of concept. Cloud & MLOps fluency: AWS/GCP/Azure, containerization, serverless architectures, and MLOps/LLMOps tooling (Weights & Biases, LangSmith, MLflow). A product‐first mindset: Fluency with ...

with the Client Developing and optimising machine learning models, AI architectures, and intelligent automation capabilities. Building and maintaining AI pipelines, model deployment processes, and MLOps capabilities. Collaborating with Data Engineers and key business stakeholders to develop high-value AI use cases. Implementing responsible AI practices, ensuring compliance with governance, security … Python skills and hands‐on experience with ML frameworks and libraries. Experience with Generative AI, LLMs, RAG, and prompt engineering . Strong understanding of MLOps, model deployment, CI/CD, and monitoring. Experience with large datasets, cloud-based data platforms, and ideally Microsoft Azure. Head Resourcing is committed to being ...

ensuring alignment with enterprise standards and technology strategy.You will also contribute to the evolution of ScottishPower's AI engineering practices, establishing reusable patterns, improving MLOps capabilities and supporting the adoption of modern cloud-native architectures. This includes AI model deployment, monitoring, automation, CI/CD pipelines and responsible AI practices … expertise in Python and modern machine learning frameworks, alongside experience with cloud platforms such as AWS and Azure. Knowledge of LLM frameworks, RAG architectures, MLOps tooling, container technologies and enterprise data platforms will be highly valued.Beyond technical capability, you will be an effective communicator who can influence both technical ...

Engineering Glasgow/Remote Consultant Contract As a Senior DevOps/MLOps Engineer at Kodamai, you will own the end-to-end infrastructure strategy, from setting up and maintaining our GCP-based internal environments to advising clients on optimal deployment architectures. You will design, implement, and continuously improve the pipelines … . Ensure high pipeline reliability, fast feedback loops, and clear rollback mechanisms. Champion DevOps best practices across engineering teams - documentation, runbooks, and post-mortems. MLOps Support the operationalization of ML models - model versioning, experiment tracking, and deployment workflows. Work with data science teams to configure MLOps tooling such as Vertex ...

JPMorganChase is seeking a Software Engineer III to join the AI/ML and Data Platform. You will be a seasoned member of an agile team delivering trusted, secure scalable technology across multiple business areas. ...

different cadences, from rapid software release cycles through to longer‐term research and development work. Apply sufficient technical fluency in machine learning (including MLOps, model deployment and training infrastructure) and software‐defined robotics (including ROS/ROS2, simulation and digital twins) to identify risks, challenge assumptions and support sound decision … comfortable engaging in technical discussions and reviews. Applied experience in machine learning and/or robotics environments, with familiarity in areas such as MLOps, deployment pipelines, model training workflows, and simulation‐to‐production delivery. Experience operating across mixed delivery cadences, supporting programmes where teams work at different speeds and delivery ...