5 of 5 Remote/Hybrid MLOps Jobs in Glasgow

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. ...

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 ...

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 ...

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 ...