51 to 75 of 115 MLOps Jobs in the UK excluding London

intensive, AI/ML applications in an industrial setting - Substantial depth and breadth in system design, latency optimization, cloud infrastructure optimization, knowledge graphs, and MLOps - Deep understanding of CS fundamentals, computational complexity and algorithm design - Experience with building large-scale distributed systems in an agile environment and the ability ...

capabilities Collaborate with engineering, data, and product teams to deliver end-to-end LLM-powered features Champion best practices in LLM engineering, MLOps, evaluation, monitoring, and observability About You: You’re a strong software engineer who is passionate about Large Language Models You have hands-on experience designing, optimising, deploying ...

data collection and analysis to implementation and monitoring of solutions facilitate adoption of ML models through exceptional communication with stakeholders work in an established MLOps team at SB there is significant opportunity to deploy end to end and will be actively encouraged continually improve through active learning and development highlighting ...

data collection and analysis to implementation and monitoring of solutions facilitate adoption of ML models through exceptional communication with stakeholders work in an established MLOps team at SB there is significant opportunity to deploy end to end and will be actively encouraged continually improve through active learning and development highlighting ...

business impact. Experience with agentic telemetry, evaluation services, and orchestration of NLQ workflows.Demonstrated leadership working with engineers, data scientists, and AI practitioners.Familiarity with MLOps practices and AI pipelines.Hands-on experience building and maintaining user interfaces for NLQ and data exploration. ABOUT US J.P. Morgan is a global leader in financial ...

impact. Experience with agentic telemetry, evaluation services, and orchestration of NLQ workflows. Demonstrated leadership working with engineers, data scientists, and AI practitioners. Familiarity with MLOps practices and AI pipelines. Hands-on experience building and maintaining user interfaces for NLQ and data exploration. ABOUT US J.P. Morgan is a global leader ...

business impact. Experience with agentic telemetry, evaluation services, and orchestration of NLQ workflows.Demonstrated leadership working with engineers, data scientists, and AI practitioners.Familiarity with MLOps practices and AI pipelines.Hands-on experience building and maintaining user interfaces for NLQ and data exploration. ABOUT US J.P. Morgan is a global leader in financial ...

impact. Experience with agentic telemetry, evaluation services, and orchestration of NLQ workflows. Demonstrated leadership working with engineers, data scientists, and AI practitioners. Familiarity with MLOps practices and AI pipelines. Hands-on experience building and maintaining user interfaces for NLQ and data exploration. ABOUT US J.P. Morgan is a global leader ...

impact.\ NExperience with agentic telemetry, evaluation services, and orchestration of NLQ workflows.\ NDemonstrated leadership working with engineers, data scientists, and AI practitioners.\ NFamiliarity with MLOps practices and AI pipelines.\ NHands-on experience building and maintaining user interfaces for NLQ and data exploration.\ N\nABOUT US\nJ.P. Morgan is a global ...

container orchestration (Kubernetes), service mesh, CI/CD, IaC, observability, and platform engineering\ NStrong knowledge of real-time streaming (Kafka/Pulsar), batch analytics, MLOps, model governance, and responsible AI patterns\ NSecurity and resiliency expertise: zero trust, secrets management, identity, encryption, chaos engineering, disaster recovery, and site reliability practices\ NIntegration ...

level of review and controls based on risk Collaborate with Cloud, Data, Networks, and Digital teams to ensure secure configuration of AI infrastructure, including MLOps and compute environments Guide programmes and product teams with clear, practical recommendations that balance security, delivery, and innovation Drive compliance with regulatory, legal, and internal ...

engineering Strong understanding of various architectures including data warehouses, data lakes, data lake houses and data mesh Strong understanding of best practice DataOps and MLOps Up-to-date understanding of various data engineering technologies including Apache Spark, Databricks and Hadoop Strong understanding of agile ways of working Up-to-date ...

Engineer and ML Engineer through to Senior, Lead, Applied Scientist and Research Engineer positions—for upcoming roles across applied machine learning, generative AI, MLOps and model productionisation. These are hands-on engineering roles where you'll build, train, ship and operate the models that drive real product behaviour and real … models at scale LLM and generative AI tooling exposure (LangChain, LlamaIndex, vector databases such as Pinecone, Weaviate or pgvector, RAG patterns, fine-tuning workflows) MLOps platforms (MLflow, Kubeflow, Vertex AI, SageMaker, Databricks or equivalent) Cloud experience (AWS, GCP, Azure), containerisation and basic data engineering/SQL Familiarity with experimentation ...

architectures and tool invocation frameworks Experience with CCaaS platforms (Google CES/CXAS, Genesys, NICE, Amazon Connect) Strong understanding of AI/ML lifecycle, MLOps, and data governance Experience working with Tier-1 banks or large financial institutions Certifications Google Cloud Professional Data Engineer (Highly Preferred) Google Professional Cloud Architect ...

Experience: Strong commercial understanding of the modern AI ecosystem, including: Generative AI Large Language Models (LLMs) Prompt Engineering Retrieval-Augmented Generation (RAG) Vector Databases MLOps Experience leading the implementation of enterprise-scale AI solutions and being accountable for delivery outcomes. Knowledge of cloud platforms such as AWS, Azure ...

Experience: Strong commercial understanding of the modern AI ecosystem, including: Generative AI Large Language Models (LLMs) Prompt Engineering Retrieval-Augmented Generation (RAG) Vector Databases MLOps Experience leading the implementation of enterprise-scale AI solutions and being accountable for delivery outcomes. Knowledge of cloud platforms such as AWS, Azure ...

modern AI technologies and concepts, including: Generative AI Large Language Models (LLMs) Prompt Engineering Retrieval-Augmented Generation (RAG) Vector Databases AI Solution Architecture MLOps Principles AI Governance and Risk Considerations Candidates are not expected to be hands-on developers but should be capable of guiding technical teams and contributing ...

that support machine learning, advanced analytics or data products. You understand the full path from data quality and governance through to pipelines, model development, MLOps and production adoption. You can work credibly with senior product, commercial and engineering leaders. You’re comfortable making trade-offs across architecture, cost, delivery pace ...

complex technical challenges into clear, business-focused insights A passion for raising engineering standards and mentoring others Experience with GA4 data modelling or supporting MLOps environments (e.g. Vertex AI) would be a bonus. About Matalan From humble beginnings to disruptive innovations in the retail industry, find out about how Matalan ...

Large Language Models (LLMs) with proprietary OT data, maintenance manuals, and historian logs for intelligent operator assistance and automated root-cause analysis. Edge AI & MLOps: Operationalize lightweight machine learning models as containerized Azure IoT Edge modules to execute real-time anomaly detection, predictive maintenance, and closed-loop control tasks directly ...

with application and integration platforms. Prior experience working at the boundary of AI/ML infrastructure and broader platform engineering, including understanding of how MLOps capabilities are hosted and operated within an enterprise platform. Experience engaging with external software vendors and system integrators on integration architecture and platform alignment. Relevant ...

avoid duplication, ensure accountability. Collaborate with Product Managers to embed AI capabilities into broader product strategies. Work closely with Data Scientists, ML Engineers, and MLOps teams to ensure technical feasibility and scalability. Act as the primary liaison between business stakeholders and AI team. Educate stakeholders on AI capabilities, limitations ...

organisations solve complex operational, technical and regulatory challenges through AI adoption at scale. You will work across multidisciplinary teams, collaborating with data scientists, architects, MLOps/LLMOps engineers, business stakeholders and senior leadership to design, deliver and scale AI products, agentic AI solutions and data driven applications. Key responsibilities: ·Building … Using Python and SQL to build robust, scalable AI and data solutions ·Working with cloud platforms such as AWS, Azure, GCP or Databricks ·Supporting MLOps, LLMOps, CI/CD and software engineering best practice ·Collaborating with technical and non-technical stakeholders across complex programmes ·Helping identify technical, delivery, security, data ...

Azure Machine Learning. Define data platform architecture, analytics pipelines, and enterprise integration patterns. Collaborate with engineering teams to implement CI/CD and MLOps best practices. Provide technical leadership, governance, and architectural guidance to stakeholders and delivery teams. Essential Skills Strong enterprise architecture experience across AWS, Azure, and hybrid/ ...

Demonstrated ability to work across complex, multi-source data environments Desirable Experience in a security, threat intelligence or data-intensive product domain Familiarity with MLOps practices and model lifecycle management Exposure to autonomous agents, agentic workflows or tool-use patterns with LLMs Experience with cloud-native infrastructure ...