19 of 19 Permanent MLOps Jobs in Glasgow

/ML/LLM/GenAI solutions to meet business objectives. Manage, mentor, and guide a team of ML and MLOps engineers. Develop and maintain automated pipelines for model deployment, ensuring scalability, reliability, and efficiency. Implement optimization strategies to fine-tune generative models for specific NLP use cases, ensuring high ...

optimize relational data models using PostgreSQL. Deploy and manage backend services on cloud platforms, preferably Azure and/or GCP. Collaborate with DevOps/MLOps engineers on CI/CD pipelines, containerization, and deployment strategies. Monitor service health and performance, proactively addressing bottlenecks and reliability issues. Work within an Agile ...

foundation with proficiency in Python, cloud platforms (AWS/Azure), APIs, and full-stack development to rapidly prototype and productionise AI applications. Experience with MLOps practices including model deployment, monitoring, evaluation, and CI/CD pipelines with delivering AI solutions from concept to production. Some other highly valued skills ...

fits into a drug‐discovery or materials‐design workflow, plus familiarity with cheminformatics tooling (e.g. RDKit, OpenEye) — or willingness to pick these up. MLOps fluency: experiment tracking, data versioning, model serving, and observability of deployed models. A visible track record in the field — peer‐reviewed publications, open‐source contributions ...

cloud data warehousing principles. Expertise with cloud data warehouse solutions such as Databrick, Snowflake or Redshift. Proven experience in DataOps, CI/CD (MLOps is a plus) Exceptional written, verbal, and listening communication skills, with the ability to effectively explain complex technical concepts to both technical and non-technical audiences. ...

/GenAI/Agentic AI topics. Open-source contributions, including maintaining or meaningfully contributing to ML/GenAI GitHub repositories (libraries, tooling, eval harnesses, MLOps components). Experience with ML accelerators and performance optimization (e.g., GPUs/TPUs), including profiling, distributed training, and inference optimization. #J-18808-Ljbffr ...

Agentic AI solutions. The ideal candidate brings a balanced mix of modern AI/ML delivery (LLMs/SLMs, RAG, tool-using agents, evaluation, MLOps) and backend/service engineering (Java and/or Python, APIs/microservices, testing, CI/CD, observability, reliability) on AWS and cloud-native platforms. … improve quality over time. Build and maintain data pipelines and processing workflows required for ML/GenAI use cases using cloud services. Apply MLOps practices across the lifecycle: experimentation, versioning, CI/CD, deployment, monitoring, and maintenance for models/prompts/agents. Implement robust testing (unit/integration), performance ...

develop GenAI/Agentic AI applications, integrate with tools and data, and ensure secure, reliable production services. The role emphasizes modern AI engineering workflows, MLOps, and scalable cloud deployments on AWS, using Java and/or Python, APIs, and CI/CD. Strong collaboration across stakeholders is essential. #J ...

security considerations. Experience using Git and modern software development practices including testing and continuous integration. Knowledge of Agile development methodologies and DevOps/MLOps principles. Strong stakeholder engagement and communication skills with both technical and nontechnical audiences. Degree, Master's qualification or equivalent experience in Artificial Intelligence, Software Engineering, Data … software in C++ within safety-critical environments. Experience defining solution architectures or technical governance. Familiarity with large-scale data management and data-labelling strategies. MLOps experience supporting production AI deployments. Contributions to open-source projects, technical publications or patents. Relevant AI, cloud or Agile certifications. With five strategic offices ...

engineering, version control and automated testing Data preparation, feature engineering and data quality techniques Machine learning methods, model selection, training and evaluation Deployment patterns, MLOps, monitoring and lifecycle management Cloud, compute and development tools used to deliver AI solutions Responsible AI, security, privacy, safety and regulatory considerations Structured approaches ...

cases (forecasting, ATP optimisation, document AI, anomaly detection). Define ML data contracts and embed models in operational processes (Fiori, SAC). Establish MLOps foundations: pipelines, CI/CD, monitoring, retraining, drift. Chair the Data & AI Design Authority, driving alignment across internal teams and multiple SI/vendor partners. Govern ...

goals, and ensure architectural integrity and commercial viability. The role requires deep knowledge of Data Lakehouses, Data Mesh, real-time analytics, and GenAI/MLOps, with a proven ability to scale #J-18808-Ljbffr ...

seeking an AI Platform & Site Reliability Engineering Managing Consultant to design, build and scale secure AI platforms. You will integrate SRE, observability and MLOps, guiding senior clients from strategy to delivery. This role offers flexible hybrid working across Manchester, Glasgow or London, with leadership of multidisciplinary teams and governance shaping ...

. You will bridge the gap between complex enterprise data strategies—spanning Cloud Migrations, Data Mesh architectures, Real-Time Analytics, and Generative AI/MLOps pipelines—and precise execution across globally distributed engineering pods of 100+ people. The Impact By pairing technical depth with executive gravitas, you will de-risk … Fluency: Deep understanding of modern data stacks (Data Lakehouses, Data Mesh, Real-time Streaming, ETL/ELT pipelines) and AI/ML lifecycle management (MLOps, GenAI integration, Model Governance). Ability to challenge architectural patterns and supervise technical teams without acting as the hands-on architect. Cloud & Platform Mastery: Expertise ...

Platform Strategy & Architecture: Assess, define and evolve enterprise AI platform architectures, covering LLM and agentic frameworks, AI gateways, model lifecycle management, data platforms, MLOps/LLMOps foundations and integration patterns. Support clients in evaluating build, buy and hybrid approaches aligned to business needs, risk appetite and operational requirements.• AI Platform … operating cloud-native, platform engineering, AI platform or reliability engineering solutions within complex enterprise environments. • Strong understanding of AI platform architectures including LLMOps, MLOps, agentic AI frameworks, model lifecycle management and AI operational controls. • Experience establishing and scaling SRE practices including observability, SLIs, SLOs, error budgets, incident management and reliability ...

Barclays is seeking an experienced MLOps Engineer to lead deployment pipelines and governance for enterprise ML initiatives. You will implement and optimize SageMaker, Databricks, and MLflow, driving reproducibility, security, and scalable AI solutions across teams. Strong emphasis on collaboration with data scientists and infra engineers. The role requires hands ...

Responsibilities MLOps Leadership : Build and evolve model deployment pipelines, registries, and monitoring frameworks. Tooling & Integration : Implement and optimize SageMaker , Databricks , MLflow , and related orchestration tools. Governance & Risk : Ensure compliance with model governance , auditability , and AI safety standards . Operational Excellence : Drive reproducibility, lineage tracking, drift detection, and rollback strategies. Hands … Delivery : Write production-grade Python code for automation, orchestration, and monitoring. Collaboration : Partner with data scientists, quants, and infra teams to align MLOps capabilities with business needs. Mentorship : Coach engineers on best practices for ML lifecycle management and secure deployment. Required Skills Strong Python engineering background. Deep experience with SageMaker ...

senior ML engineer to design, develop and improve software for business and technology capabilities, focusing on scalable, secure ML deployments. You will lead MLOps with Python, SageMaker, Databricks, MLflow, kubernetes, and collaborate with data scientists, infra teams; ensure governance and risk management. The role emphasizes hands-on delivery and mentorship ...

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 ...