201 to 225 of 696 MLOps Jobs in England

practices, including testing, version control and code reviews. Exposure to cloud platforms, containerisation and modern deployment practices. Knowledge of CI/CD, DevOps or MLOps principles would be highly beneficial. Excellent communication skills and the ability to engage effectively with both technical and business stakeholders. Degree in a quantitative discipline ...

needed to put machine learning into production at scale. You'll have a meaningful say in how the deployment framework, model registry and wider MLOps capability are built. You won't simply inherit someone else's setup. If you've reached the point where you want to build the systems ...

needed to put machine learning into production at scale. You'll have a meaningful say in how the deployment framework, model registry and wider MLOps capability are built-not simply inherit someone else's setup. If you've reached the point where you want to build the systems behind production ...

associated architecture patterns Experience designing and managing APIs including REST and GraphQL Strong understanding of microservices and serverless architectures Practical experience with DevOps and MLOps practices including CI/CD pipelines Knowledge of security frameworks, identity management and authentication standards Desirable Skills for the AI Solutions Architect: Experience with enterprise ...

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 ...

artificial intelligence 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, TensorRT, or ONNX.Experience with ...

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 ...

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 ...

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 ...

advanced AI, and specifically: The management of people, process, documentation associated with the development, deployment, maintenance, training and operational management of Machine Learning solutions (MLOps) The management of people, process, documentation, maintenance and operational management of Generative AI solutions (GenAIOps/LLMOps) Manage, objectively assess and track Technology Readiness Levels ...

/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 ...

PyTorch) Understanding of core ML concepts — model training, evaluation, and production trade-offs — enough to design robust interfaces around them Experience with DevOps and MLOps Degree-level qualification in computer science or related field Deep understanding of Python Familiarity with Linux, bash, and the command line Ability to write logical ...

writing clean, efficient, and modular code suitable for production environments. Desirable Skills or Knowledge Familiarity with AMO physics or quantum machine learning. Experience with MLOps best practices. Knowledge of systems like Apache Kafka, MQTT or real‐time data pipelines. Experience with cloud platforms (AWS, Azure, GCP). Deep expertise ...

cost management Model serving and inference at production scale (eg KServe, RayServe, Triton, vLLM, or similar) with real latency and cost constraints(preferred RayServe) MLOps pipeline tooling - training pipelines, model registries, feature stores, and lineage (Kubeflow, MLflow, Feast, Weights & Biases, or equivalents) LLMOps in production - inference serving, prompt/version ...