51 to 75 of 173 Anomaly Detection Jobs in the UK

tasks. AIOps & Intelligent Observability: Ability to integrate traditional observability stacks (e.g., Datadog, Prometheus, OpenTelemetry) with AI/ML models to automate root-cause analysis, anomaly detection, and semantic log clustering. Self-healing Infrastructure Engineering: Experience designing closed-loop, self-healing systems that autonomously execute recovery actions (e.g. ...

tasks. AIOps & Intelligent Observability: Ability to integrate traditional observability stacks (e.g., Datadog, Prometheus, OpenTelemetry) with AI/ML models to automate root‐cause analysis, anomaly detection, and semantic log clustering. Self‐Healing Infrastructure Engineering: Experience designing closed‐loop, self‐healing systems that autonomously execute recovery actions (e.g. ...

remediation logic.* Improve enterprise tool signal quality by integrating vendor health data and usage telemetry into AIOps systems.* Support tuning of alert correlation and anomaly detection models for enterprise services.* Track improvements in MTTR, alert noise reduction, automation coverage, and platform uptime. **Requirements** * Bachelor’s degree ...

join our growing AML Team in London.This role is a unique opportunity to work behind the scenes of company transactions, develop our risk detection and assessment to the next level regarding regional typology understanding and at the same time, provide our customers with the seamless service they deserve. … capabilities.Here’s how you’ll be contributing:Innovate and Develop: Lead the development and deployment of machine learning models, including neural networks, anomaly detection, graph-based models, Transformers. Design and build modular detection systems able to detect in an evidenceable way red flags ...

Lambda, Aurora, Airflow, Terraform). Ensure data availability, integrity, and performance as the platform scales globally. Drive best practices in data quality, automated testing, anomaly detection, and CI/CD. Partner with Product, Engineering, and Commercial teams to translate data needs into platform capabilities. EHR Integration ...

Monitor adherence to risk and control guidelines for data access and use.Data quality, reliability, and lineage - Define data contracts for telemetry sources; implement validation, anomaly detection, and reconciliation; document definitions (e.g., formulas, thresholds) and end-to-end data lineage from raw signals to KPIs and insights.Automation ...

driven decisions . Implement feedback loops to continuously improve model performance . AI-Driven SRE Enablement Develop ML/AI models for anomaly detection, incident prediction, and root cause analysis across distributed systems . Implement intelligent alerting systems to reduce noise and improve signal fidelity . Build ...

including model lifecycle management, CI/CD for ML, feature stores, model monitoring, experimentation, and deployment pipelines. Experience with AIOps, intelligent observability, automated remediation, anomaly detection, and AI‐assisted cloud operations. Experience with cloud security, compliance, and governance frameworks, including considerations specific to AI‐enabled workloads. Google ...

MLOps, including model lifecycle management, CI/CD for ML, feature stores, model monitoring, experimentation, and deployment pipelines.Experience with AIOps, intelligent observability, automated remediation, anomaly detection, and AI-assisted cloud operations.Experience with cloud security, compliance, and governance frameworks, including considerations specific to AI-enabled workloads.Google Cloud certifications ...

correctness are non‐negotiable Designing and enforcing data integrity frameworks across betting, trading, and customer data domains – including reconciliation between source and target systems, anomaly detection, and audit trail requirements Overseeing the implementation of regulatory and compliance reporting pipelines that meet the requirements of the UK Gambling ...

correctness are non‐negotiable Designing and enforcing data integrity frameworks across betting, trading, and customer data domains – including reconciliation between source and target systems, anomaly detection, and audit trail requirements Overseeing the implementation of regulatory and compliance reporting pipelines that meet the requirements of the UK Gambling ...

telemetry-driven automation and blameless postmortems. Champion AI‐enabled operations using AWS Bedrock/SageMaker and Model Context Protocol (MCP) for log analysis, anomaly detection, incident triage, and workflow orchestration; establish adoption guardrails. Define and enforce cloud guardrails and security controls (SCPs/IAM boundaries, OPA policies ...

telemetry-driven automation and blameless postmortems. Champion AI‐enabled operations using AWS Bedrock/SageMaker and Model Context Protocol (MCP) for log analysis, anomaly detection, incident triage, and workflow orchestration; establish adoption guardrails. Define and enforce cloud guardrails and security controls (SCPs/IAM boundaries, OPA policies ...

AIOps: Translate NFRs into pragmatic architecture decisions, define SLOs/SLIs, and design modern observability (metrics, logs, traces). Apply AIOps for alert reduction, anomaly detection, incident triage, and continuous improvement across hybrid and cloud platforms. Advisory & communication: Simplify complexity for senior audiences; align diverse viewpoints ...

request, model and agent error rates, retrieval quality and drift, with dashboards and alerting that surface problems before users do. FinOps: Embed cost estimation, anomaly detection and resource optimisation into the pipeline as a default rather than a monthly review. Security posture: Apply zero-trust networking, least ...

request, model and agent error rates, retrieval quality and drift, with dashboards and alerting that surface problems before users do. FinOps: Embed cost estimation, anomaly detection and resource optimisation into the pipeline as a default rather than a monthly review. Security posture: Apply zero-trust networking, least ...

request, model and agent error rates, retrieval quality and drift, with dashboards and alerting that surface problems before users do. FinOps: Embed cost estimation, anomaly detection and resource optimisation into the pipeline as a default rather than a monthly review. Security posture: Apply zero-trust networking, least ...

request, model and agent error rates, retrieval quality and drift, with dashboards and alerting that surface problems before users do. FinOps: Embed cost estimation, anomaly detection and resource optimisation into the pipeline as a default rather than a monthly review. Security posture: Apply zero-trust networking, least ...

resiliency, cyber risk reduction, and operational efficiency.Serve as the executive sponsor for AI-enabled operational transformation initiatives.Autonomous Operations TransformationImplement AI-driven monitoring, event correlation, anomaly detection, and root-cause analysis capabilities.Lead the deployment of autonomous operational workflows capable of identifying, diagnosing, and resolving incidents without human intervention.Establish ...

technology teams to optimise semantic models for AI readiness. Apply data science, machine learning, NLP, and advanced analytics techniques to enhance insight generation and anomaly detection. Contribute to the organisation's long-term Data & AI strategy, influencing senior stakeholders and executive leadership. Establish best practices, standards, and governance frameworks ...

technology teams to optimise semantic models for AI readiness. Apply data science, machine learning, NLP, and advanced analytics techniques to enhance insight generation and anomaly detection. Contribute to the organisation's long-term Data & AI strategy, influencing senior stakeholders and executive leadership. Establish best practices, standards, and governance frameworks ...

performance intelligence, and financial reconciliation across all travel verticals.Architect and evolve reference pricing systems leveraging predictive modeling, elasticity signals, competitive benchmarking, and real-time anomaly detection.Build the supplier intelligence layer, transforming transactional data into automated performance signals, demand insights, and pricing diagnostics.Establish and govern the canonical source of truth … framing, signal design, evaluation metrics (e.g., precision/recall tradeoffs, forecasting accuracy), monitoring frameworks, drift detection, and retraining strategies.Experience shipping predictive systems, anomaly detection platforms, automated decisioning infrastructure, or model-driven pricing systems at scale.Experience owning pricing systems, financial reporting platforms or marketplace intelligence tools.Deep ...

intelligence, and financial reconciliation across all travel verticals. Architect and evolve reference pricing systems leveraging predictive modeling, elasticity signals, competitive benchmarking, and real-time anomaly detection. Build the supplier intelligence layer , transforming transactional data into automated performance signals, demand insights, and pricing diagnostics. Establish and govern the canonical source … signal design, evaluation metrics (e.g., precision/recall tradeoffs, forecasting accuracy), monitoring frameworks, drift detection, and retraining strategies. Experience shipping predictive systems, anomaly detection platforms, automated decisioning infrastructure, or model-driven pricing systems at scale. Experience owning pricing systems, financial reporting platforms or marketplace intelligence ...

stack) Integrate knowledge graph retrieval & reasoning into LLM/RAG/GraphRAG systems Develop and evaluate graph ML/embedding models (link prediction, anomaly detection) Optimize graph performance, scaling, and query efficiency Liaise with client stakeholders: translate business problems into graph solutions Mentor junior engineers, contribute ...

world, production problems Strong Python skills (or a similar language used in applied data science) Experience with supervised learning and unsupervised techniques such as anomaly detection Exposure to cloud‐based model training, deployment and automation A strong sense of ownership and accountability for data science outputs Experience ...