226 to 250 of 435 MLOps Jobs in London

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 ...

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 ...

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 ...

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 ...

/AI patterns, define and evangelise best practices, and establish reusable approaches that enhance delivery of AI use cases across the business Drive MLOps standards and practices across teams, including CI/CD for models, automated testing, monitoring, and deployment pipelines Collaborate with enterprise platform and data science teams, contributing … culture of continuous learning What we're looking for Deep expertise in Python and ML engineering Deep expertise in ML/AI systems design, MLOps, and cloud-native architectures Track record of leading ML/AI technical initiatives across multiple teams Strong experience with cloud platforms (AWS, Azure, GCP) Experience ...

emerging ML/AI patterns, define and evangelise best practices, and establish reusable approaches that enhance delivery of AI use cases across the businessDrive MLOps standards and practices across teams, including CI/CD for models, automated testing, monitoring, and deployment pipelinesCollaborate with enterprise platform and data science teams, contributing … engineers, fostering a culture of continuous learningWhat we're looking forDeep expertise in Python and ML engineeringDeep expertise in ML/AI systems design, MLOps, and cloud-native architecturesTrack record of leading ML/AI technical initiatives across multiple teamsStrong experience with cloud platforms (AWS, Azure, GCP)Experience with ...

Microsoft Fabric where appropriate for data preparation, experimentation and deployment support. Follow good engineering practices for version control, testing, documentation and reproducibility. Contribute to MLOps ways of working, including model versioning, deployment handover and performance monitoring. Consider model explainability, data quality, bias, privacy and responsible AI implications where relevant. What … work collaboratively in mixed technical and business teams. Nice to have: Exposure to pilot or production deployment of machine learning models. Good understanding of MLOps concepts such as versioning, reproducibility, monitoring and deployment handover. Experience with at least one modern cloud data or analytics platform, such as Snowflake, Databricks, Microsoft ...

Make your mark at one of the biggest names in payments. We're looking for an AI Engineer to join our AI Innovation & Technology team . You'll work at the forefront of AI, exploring ...

Ready to take your career global Make your mark at one of the biggest names in payments. We're looking for an AI Engineer to join our AI Innovation & Technology team. You'll work at ...

GenAI initiatives, building LLM-based applications, prompts, fine-tuning and retrieval augmented generation for enterprise use cases. You will collaborate with data engineers and MLOps, deploy AI workflows via APIs or web apps, and stay at the forefront of GenAI with tools like LangChain, Hugging Face and OpenAI, ensuring scalable ...

powering highly personalised customer experiences, including recommendation algorithms, retrieval systems and LLM-powered conversational AI. You will work closely with Product Managers, Engineers and MLOps to move ideas from experimentation into #J-18808-Ljbffr ...

involves designing, deploying, and optimising machine learning models and AI workflows within Azure. The ideal candidate will have experience with Azure Databricks, LangChain, and MLOps practices. The role offers an attractive salary of up to £82,000, an annual performance bonus, and a comprehensive benefits package including private medical insurance ...

exposure is beneficial Why Join? Up to £90,000 + 7% bonus Hybrid working (2 days/week) High commercial ownership and visibility Dedicated MLOps support Opportunity to shape strategy through machine learning If you're looking for a role where your models directly influence customer experience, commercial performance ...

/ML lifecycles **Strong experience with data quality, metadata management, master data management, knowledge graphs, or data products **Familiarity with Generative AI, MLOps, AI governance, model risk management, and responsible AI frameworks Bonus: **Background in financial services, risk, ratings, or regulated data environments Immediate start date. If interested, please apply ...

level optimisation Broader ML background spanning areas beyond deep learning Experience with large-scale transactional or financial data Background in ML infrastructure or MLOps Please include a CV in English Curious to learn more about Klarna and what it's like to work here? Explore our career site! klarna.com ...

technical foundations that define how machine learning models are delivered and operated in production. Why this role matters As the technical lead within our MLOps function, you will shape how ML is engineered in practice. This role will focus on establishing the standards, patterns, and technical approaches that enable models ...

customer requirements with platform capabilities. AI/ML infrastructure, GPU platforms, distributed training, inference workloads, or high‐performance computing (HPC) environments. Kubernetes platform engineering, MLOps, or cloud‐native platform operations. Infrastructure automation and Infrastructure‐as‐Code technologies. Large‐scale storage, high‐performance networking, or data‐centre infrastructure. Product strategy, roadmap ...