401 to 425 of 748 MLOps Jobs in England

networks, graph-based and sequence-based architectures Providing technical leadership to raise levels of expertise and best practices, including mentoring others Working with the MLOps team to drive tooling for rapid model iteration and lifecycle optimisation You should apply if What we’re doing at Monzo excites you. Multiple-year ...

areas including: Artificial Intelligence and Machine Learning Data Science and advanced analytics Generative AI and Large Language Models Modern AI/ML engineering practices MLOps and production AI Emerging technologies and developments across the AI landscape You'll work with technical audiences ranging from engineers and data professionals through ...

degree or PhD in a quantitative discipline, e.g. Computer Science, Mathematics, StatisticsTrack record of developing, deploying business critical machine learning models Broad knowledge of MLOps tooling – for versioning, reproducibility, observability etcExperience monitoring, maintaining, enhancing existing models over an extended time periodSpecialism in NLP or Computer VisionSolid understanding of fundamentals ...

AI. Experience developing AI-enhanced digital assistant solutions such as Microsoft Copilot. Practical experience with Retrieval-Augmented Generation (RAG) architectures and Machine Learning Operations (MLOps). Strong stakeholder engagement skills, with the ability to communicate technical solutions clearly to both technical and non-technical audiences. It would be great ...

technical or business field. Bonus qualifications Experience with AI accelerators or GPUs LLM fine‐tuning or inference knowledge Familiarity with Sambanova products or similar MLOps platforms Enterprise SaaS or PaaS background MBA or advanced technical degree Base Salary Range £94,500—£115,000 GBP Benefits Summary SambaNova offers a competitive ...

multi cloud with strong AWS depth). Deep understanding of AI/ML solution patterns (data pipelines, training, inference, monitoring, and MLOps) and how they map onto AWS services (e.g. SageMaker, Batch, Step Functions, EKS/ECS, data lake components). Proven ability to take research and prototype environments ...

multi cloud with strong AWS depth). Deep understanding of AI/ML solution patterns (data pipelines, training, inference, monitoring, and MLOps) and how they map onto AWS services (e.g. SageMaker, Batch, Step Functions, EKS/ECS, data lake components). Proven ability to take research and prototype environments ...

workloads on AWS (or multi‐cloud with strong AWS depth).Deep understanding of AI/ML solution patterns (data pipelines, training, inference, monitoring, and MLOps) and how they map onto AWS services (e.g. SageMaker, Batch, Step Functions, EKS/ECS, data lake components).Proven ability to take research and prototype ...

workloads on AWS (or multi‐cloud with strong AWS depth).Deep understanding of AI/ML solution patterns (data pipelines, training, inference, monitoring, and MLOps) and how they map onto AWS services (e.g. SageMaker, Batch, Step Functions, EKS/ECS, data lake components).Proven ability to take research and prototype ...

workloads on AWS (or multi‐cloud with strong AWS depth).Deep understanding of AI/ML solution patterns (data pipelines, training, inference, monitoring, and MLOps) and how they map onto AWS services (e.g. SageMaker, Batch, Step Functions, EKS/ECS, data lake components).Proven ability to take research and prototype ...

workloads on AWS (or multi‐cloud with strong AWS depth).Deep understanding of AI/ML solution patterns (data pipelines, training, inference, monitoring, and MLOps) and how they map onto AWS services (e.g. SageMaker, Batch, Step Functions, EKS/ECS, data lake components).Proven ability to take research and prototype ...

technical delivery while also leading multiple projects or workstreams in parallel. Desirable • Experience establishing or embedding best practice in ML engineering, software delivery or MLOps within a team or function. • Experience in regulated, enterprise or operationally complex environments. • Experience promoting reuse through shared frameworks, tooling, templates or engineering standards. • Experience ...

well and partner with the commercial leadership to deliver high-value AI driven opportunities that enhance efficiency, customer experience, and growth.Establish best practices for MLOps, experimentation, model monitoring, and continuous optimisation.Data Engineering & Architecture Oversee the design and delivery of scalable, secure, and modern data platforms, pipelines, and architectures.Ensure data availability ...

Engineering, Telecommunications, Computer Science, Artificial Intelligence or a related discipline, or equivalent practical experience. Professional certifications or continued learning in AI, machine learning, MLOps or AI safety. What's in it for you? Competitive salary and bonus opportunity. 25 days annual leave with the option to purchase ...

robotics, or a related field Authored publications in top-tier journals and conferences (e.g. CVPR, NeurIPS, ICLR, ICCV, ICRA, IROS, TPAMI) Experience working with MLOps infrastructure or deploying AI models to production, including the associated monitoring, testing and quality assurance An understanding of different sensor modalities, their trade-offs ...

environment Prior experience working with highly imbalanced datasets for binary classification problems Event streaming data engineering tools such as Flink or Spark Experience with MLOps and tools such as Airflow, BentoML and MLFlow Use of Docker, CI/CD and SDLC (e.g. JIRA, Git, Jenkins, Octopus) Why Zempler Bank What ...

MLOps Technical Manager West London, UK (Hybrid – 2-3 days on-site)| Permanent About the Role We're recruiting on behalf of a major, well-established organisation for an MLOps Technical Manager to lead a growing ML engineering function. This is a genuinely interesting project: you'll work closely with … scalability, reliability, and alignment with business goals Drive the ongoing migration from MLflow to AWS SageMaker, maintaining continuity and minimising disruption Define and enforce MLOps best practices across model training, serving, monitoring, and deployment MLOps & Engineering Design and maintain scalable ML infrastructure supporting batch and real-time environments Oversee ...

Rentokil Initial Group is scaling its Group AI Team to deliver production‐grade AI solutions. You will lead engineering, mentor engineers, and drive end‐to‐end implementation of agentic AI systems across global operations. You ...

Marlin Selection Ltd seeks an expert in ML-driven forecasting to develop and productionise time-series models for energy generation. You will build forecasting pipelines, collaborate with data engineering to onboard data feeds and monitor ...

government, and national security. Working alongside Data Scientists, Software Engineers, and AI specialists, you'll take projects from experimentation through to production, leveraging modern MLOps and LLMOps practices on AWS to build scalable, secure AI systems. What You'll Be Doing Design, develop, and deploy machine learning models for traditional … frameworks. Lead experimentation cycles, defining hypotheses, running evaluations, and iterating on models before production deployment. Develop robust ML pipelines using AWS services and MLOps tooling. Implement experiment tracking, model versioning, monitoring, and reproducibility best practices. Build feature engineering pipelines and contribute to feature store development. Monitor production models, optimise performance ...

deploying machine learning solutions into cloud-native production environments Experience with containerisation technologies such as Docker and orchestration platforms including Kubernetes Knowledge of modern MLOps practices including CI/CD, version control (Git) and infrastructure automation Experience working with cloud platforms and modern data ecosystems (Azure and Databricks experience beneficial … solutions in a commercial environment Experience deploying machine learning models within regulated industries such as financial services or insurance Exposure to enterprise-scale MLOps and cloud infrastructure Experience contributing to platform architecture and engineering best practices ...

delivery of AI projects end to end from the silicon and data center design that underpins AI workloads, through the software and MLOps stack, to the governance frameworks that make AI trustworthy and defensible at scale. This is a technical hardware-and-software architect role, not a strategy-only position. … parallel/high-throughput storage, and orchestration Design the AI software stack: training and fine-tuning pipelines, distributed training frameworks, inference/serving platforms, MLOps/LLMOps tooling, vector databases, and retrieval-augmented generation (RAG) and agentic architectures. Define and AI governance frameworks covering model risk management, responsible AI, data ...

enterprise data integrationsFine-tune, optimize, and deploy large language and multi-modal models with a focus on scalability, reliability, and low-latency performanceBuild scalable MLOps pipelines and cloud-native AI applications to support secure, production-grade deploymentsCollaborate with clients and cross-functional teams to shape AI engineering strategy and accelerate … models within enterprise or large-scale production environmentsStrong software engineering expertise in Python, including backend development, API design, and distributed systemsSolid understanding of scalable MLOps, observability, CI/CD practices, and cloud-native AI deployment architecturesExcellent communication, stakeholder management, and problem-solving skills within agile delivery environmentsBonus Points ForExperience building ...

workflows. Oversee the full technical lifecycle, from solution design and model selection through to deployment, monitoring and continuous improvement. Design and implement robust MLOps and LLMOps practices, including CI/CD, model monitoring, evaluation and governance. Deploy and optimise models using cloud-based AI services such as AWS Bedrock, Azure … LangChain, LangGraph or similar open-source AI toolkits . Experience fine-tuning, evaluating, deploying and monitoring large-scale AI systems. Strong understanding of MLOps and/or LLMOps , including CI/CD, model deployment, monitoring, evaluation and governance. Experience designing AI systems with a strong focus on security, responsible ...

Senior ML Engineer will own the journey from experimentation and hypothesis testing through to secure, production-grade deployment, using a modern AWS-based MLOps and LLMOps platform.The RoleThe successful candidate will balance rapid experimentation with production readiness, prototyping and validating machine learning and generative AI approaches while ensuring successful models … formulation, A/B testing and statistical evaluationProven experience moving models from experimentation into production with appropriate governance and quality controlsHands-on experience with MLOps tooling such as MLflow, Weights & Biases or Data Version ControlPractical experience building LLM/GenAI applications, including prompt engineering and retrieval-augmented generation (RAG)Familiarity ...