151 to 175 of 530 MLOps Jobs in London

pose estimation.Multi-object localisation or multi-modal perception systems.GPU optimisation, CUDA, inference acceleration or edge AI.Real-time computer vision systems.Docker, Kubernetes, Kubeflow or MLOps pipelines.Aerial, satellite or ISR imagery.Synthetic data generation, Unreal Engine or NVIDIA Omniverse.Distributed training and large-scale model optimisation.Defence, government or national security projects.An ...

object localisation or multi-modal perception systems. GPU optimisation, CUDA, inference acceleration or edge AI. Real-time computer vision systems. Docker, Kubernetes, Kubeflow or MLOps pipelines. Aerial, satellite or ISR imagery. Synthetic data generation, Unreal Engine or NVIDIA Omniverse. Distributed training and large-scale model optimisation. Defence, government or national ...

even under early-stage constraints. Nice to Have Experience with GPU cluster management and ML training workloads (NVIDIA, CUDA, distributed training). Familiarity with MLOps tooling: Experiment tracking (MLflow, Weights & Biases). Workflow orchestration (Airflow, Prefect, Argo). Data versioning (DVC). Background in scientific computing or HPC environments. Prior ...

concepts. Excellent programming skills in Python. Demonstrated experience in building, validating, deploying, monitoring, and updating production ML and AI models. Hands-on experience with MLOps and AIOps infrastructure and tooling. Proficient in problem-solving and analytical reasoning. Exceptional communication and collaboration skills. Experience with ML frameworks such as TensorFlow, PyTorch ...

apply those patterns within Azure Experience with Azure API Management (APIM) is preferred, especially as a governance or integration boundary Familiarity with AgentOps, MLOps or GenAIOps concepts, including monitoring, evaluation and feedback loops Strong collaboration skills, with the ability to influence platform standards and enable other engineering teams A pragmatic ...

Preferred Skills and Qualifications Experience working with cloud platforms such as Azure, AWS, or Google Cloud.Background in deploying machine learning models into production environments (MLOps experience is advantageous).Hands‐on experience with big‐data or distributed computing tools such as Spark or Databricks.Familiarity with visualisation tools such as Power ...

systems, or cognitive architectures.Solid understanding of software engineering principles, including design patterns, data structures, and algorithms.Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and MLOps practices is a plus.Excellent problem-solving skills, with the ability to translate complex business requirements into technical solutions.Strong communication and collaboration skills, with the ability ...

making pragmatic architectural trade-offs. Strong cloud experience, ideally within Azure. Experience deploying, monitoring and maintaining ML or LLM models in production. Familiarity with MLOps, LLMOps, evals, monitoring and lifecycle management. Hands-on experience orchestrating LLM workflows using frameworks such as LangChain. An interest in how agentic software development ...

retrieval systems Familiarity with scientific, biomedical, or scholarly datasets Experience with knowledge graphs, ontologies, or semantic enrichment systems Exposure to production ML systems and MLOps practices Academic or industry research experience in NLP, information retrieval, search, or generative AI Experience working in content-rich, knowledge-intensive, or highly regulated domains ...

engineering, including production ownership of Kubernetes-based systems. Have hands‐on experience operating AI or ML systems in production — model serving, LLM inference, or MLOps pipelines — a strong plus. Are strong in Python for automation and operational tooling, with production experience in Terraform and at least one major cloud provider ...

OECD AI Principles, model risk management, or responsible AI frameworks. Experience with AI red teaming, model evaluation, bias testing, explainability tooling, monitoring tools, MLOps, LLMOps, or AI governance platforms. Experience advising on third-party AI tools, enterprise AI platforms, or AI-enabled vendor solutions. Certifications or advanced training ...

OECD AI Principles, model risk management, or responsible AI frameworks.Experience with AI red teaming, model evaluation, bias testing, explainability tooling, monitoring tools, MLOps, LLMOps, or AI governance platforms.Experience advising on third-party AI tools, enterprise AI platforms, or AI-enabled vendor solutions.Certifications or advanced training in AI/ML, data ...

first approach.PreferredDomain exposure in media, or another enterprise vertical.Familiarity with AI safety, responsible AI frameworks, or enterprise AI governance.Familiarity with AI deployment infrastructure and MLOps tooling.#LI-EUAbout AccentureAccenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core ...

controls, auditability.Reliability engineering — SLIs/SLOs, incident management, cost and capacity management (incl. LLM token/credit cost control).Nice‐to‐have skillsLLMOps/MLOps — model serving, gateway/proxy management, prompt/version management, guardrails at runtime.Experience hosting AI/agentic platforms as a shared internal service (multi-tenant ...

/CD Technical environment (Not hands-on coding daily, but technically credible) Cloud: AWS/Azure/GCP AI/ML delivery: model deployment, MLOps/LLMOps, monitoring, iteration Platform foundations: Kubernetes (EKS/AKS), CI/CD, GitOps concepts Observability: metrics, logs, tracing; dashboards and alerting disciplines Architecture: APIs ...

engineering — SLIs/SLOs, incident management, cost and capacity management (incl. LLM token/credit cost control). Nice‐to‐have skills LLMOps/MLOps — model serving, gateway/proxy management, prompt/version management, guardrails at runtime. Experience hosting AI/agentic platforms as a shared internal service (multi ...

following AI/ML frameworks: Huggingface, Torch, Tensforflow, Scikit-Learn, and SpacyExperience in of any of the following additional platforms related to MLOps: MLFlow, dbt, Docker, Kubernetes.Experience with working cloud environments such as AWSTechnical Concept RequirementsExperience in any of the following AI/ML topics within a work context: Natural ...

OECD AI Principles, model risk management, or responsible AI frameworks. Experience with AI red teaming, model evaluation, bias testing, explainability tooling, monitoring tools, MLOps, LLMOps, or AI governance platforms. Experience advising on third-party AI tools, enterprise AI platforms, or AI-enabled vendor solutions. Certifications or advanced training ...

following AI/ML frameworks: Huggingface, Torch, Tensforflow, Scikit-Learn, and SpacyExperience in of any of the following additional platforms related to MLOps: MLFlow, dbt, Docker, Kubernetes.Experience with working cloud environments such as AWSTechnical Concept RequirementsExperience in any of the following AI/ML topics within a work context: Natural ...

optimisation and high‐security solutions for enterprise clients Expertise in implementing SSO across a variety of industry standard software Preferred/Bonus Experience with MLOps/LLMOps (Softwares such as Sagemaker, Kubeflow or ZenML). Deployment of on‐premise Kubernetes Prometheus (or other stacks) observability Experience with AWS Karpenter & Compute ...

following AI/ML frameworks: Huggingface, Torch, Tensforflow, Scikit-Learn, and Spacy* Experience in of any of the following additional platforms related to MLOps: MLFlow, dbt, Docker, Kubernetes.* Experience with working cloud environments such as AWSTechnical Concept Requirements* Experience in any of the following AI/ML topics within ...

development and deployment of ML and AI models to prove and deliver tangible commercial value. Act as a technical bridge between Data Science and MLOps to embed best practice deployment techniques across the team. Take hands-on ownership of big projects, working with a large, event-based data set. Research ...

development and deployment of ML and AI models to prove and deliver tangible commercial value. Act as a technical bridge between Data Science and MLOps to embed best practice deployment techniques across the team. Take hands-on ownership of big projects, working with a large, event-based data set. Research ...

subject matter expert regarding GCP’s latest ML/AI tooling, and continuously prototyping and advocating for new tooling to ensure that our MLOps and AI infrastructure remains fit for purpose. Collaborating with Platform Engineering and Product to prioritise relevant workstreams across the Beatport Group. Working in conjunction with ...

retrieval systems. Familiarity with scientific, biomedical, or scholarly datasets. Experience with knowledge graphs, ontologies, or semantic enrichment systems. Exposure to production ML systems and MLOps practices. Academic or industry research experience in NLP, information retrieval, search, or generative AI. Experience working in content‐rich, knowledge‐intensive, or highly regulated domains. ...