1 to 25 of 32 Permanent MLOps Jobs in Scotland

/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 ...

experience and a track record of building deployment and release automation Experience serving, scaling, and monitoring ML models or data-intensive services in production (MLOps) Practical experience with observability tooling (metrics, logging, tracing) and production incident response Experience operating ML/LLM workloads in production (LLMOps, inference reliability, cost/ ...

Science, Engineering, or a related technical field (or equivalent applied experience) Preferred qualifications, capabilities, and skills Experience operating ML/LLM workloads in production (MLOps, inference reliability, cost/performance management) Experience within financial services technology Familiarity with JPM-internal platform, cloud, and observability tooling for internal candidates #J ...

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 ...

Skills: Hands-on experience with data lineage tools and techniques, including graph & vector databases and metadata management platforms Hands-on experience with LLM Ops, MLOps, and AI/ML platform deployment at scale Familiarity with business-led analytics delivery models and rapid prototyping frameworks Experience with AI/ML governance ...

Bridging Data & AI: A solid understanding of how AI fits into the broader data landscape, with an interest in integrating LLMs and AI/MLOps into client solutions. Collaborative Leadership: A proven track record of supporting multi-squad deliveries, guiding stakeholders through architectural trade-offs, and contributing to technical proposals. ...

preparation, feature engineering, and feature stores, and understand why reproducibility and point-in-time correctness matter more than they first appear Have worked with MLOps in practice rather than in principle: model versioning and registries, experiment tracking, automated retraining, and monitoring for drift and model degradation (MLflow, SageMaker, Databricks ...

curiosity to learn others. Bridging Data & AI: Understanding how AI fits into the broader data landscape, interest in integrating LLMs and AI/MLOps into client solutions. Collaborative Leadership: Proven track record of supporting multi-squad deliveries, guiding stakeholders through architectural trade-offs, and contributing to technical proposals. Our Benefits ...

Skills: Hands-on experience with data lineage tools and techniques, including graph & vector databases and metadata management platforms Hands-on experience with LLM Ops, MLOps, and AI/ML platform deployment at scale Familiarity with business-led analytics delivery models and rapid prototyping frameworks Experience with AI/ML governance ...

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. ...

CreateFuture seeks a Senior Data Engineer with an ML focus to own data and feature pipelines for production ML systems. You will collaborate with data scientists and ML engineers, mentor junior engineers, and elevate engineering ...

/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 ...

generation (RAG) architectures and agent frameworks such as Google ADK and LangChain, helping transform how the Bank serves customers and empowers colleagues. Data engineering, MLOps and intelligent automation , creating resilient data pipelines, scalable deployment architectures and automated machine learning workflows using contemporary engineering and CI/CD practices, enabling … machine learning AI literacy Ensemble methods & model optimisation Databases & big data Neural networks & deep learning LLM foundations, engineering,customisationand integration Generative and Agentic AI MLOps& cloud computing It’sall live lectures, tutor-led groupworkand real-world challenges from day one. Learn it. Apply it. Keep going Your personal learning plan ...

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 ...

Personalize team in the UK. You will shape and own medium-sized ML features end-to-end, from framing to deployment, working with ML, MLOps and software engineering to deliver impactful personalization capabilities. The role emphasizes productionising models as APIs, real-time and batch processing, and ongoing research. ...

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 ...

. 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 ...