1 to 25 of 79 Permanent Anomaly Detection Jobs in London

stories, acceptance criteria, and prioritized backlog items across the AIOps product lifecycle. Ownership of high-impact AIOps capabilities, including noise reduction, event management, correlation, anomaly detection, root cause analysis, predictive alerting, and self-healing remediation. Partner across Operations, infrastructure, SRE, platform engineering, service management, application teams, security … experience in product management, IT operations, SRE, observability, platform engineering, or related enterprise technology roles. Strong understanding of AIOps concepts, including event correlation, anomaly detection, root cause analysis, noise reduction, predictive analytics, and automated remediation. Experience defining product roadmaps, managing requirements and backlog priorities, and delivering technical ...

fraud interventions, balancing customer experience against loss reduction. Size fraud typologies across our product lines to inform prioritization and investment decisions. Build and maintain anomaly detection systems to surface novel fraud vectors before they scale. Work closely with fraud operations, engineers, product managers, and data analysts … technical findings clearly to non-technical stakeholders and translate insights into action. What Success Looks Like in This Role: Production-grade ML models and anomaly detection systems that effectively surface and mitigate novel fraud vectors before they scale. Well-designed experiments that successfully balance customer experience against ...

building practical tools to identify and resolve issues before they reach customers. The Role You will: Design and develop data quality checks covering validation, anomaly detection, freshness and coverage. Own and improve production web-scraping infrastructure, including Playwright, proxies and anti-bot handling. Build and maintain data … ship solutions efficiently. About You Strong Python and SQL skills, with hands-on dbt and BigQuery experience. Proven experience designing data quality, validation and anomaly detection processes. Production experience with web scraping at scale, ideally using Playwright or an equivalent. Experience with big data sets ...

resolution Encode triage logic into automation pipelines so future instances are classified and escalated without manual intervention Apply ML and AI techniques — crash clustering, anomaly detection, pattern recognition — to build smarter triage pipelines Build debug tooling, scripts, and CI/CD integrations that surface OS-level signals … Exposure to automation frameworks, CI/CD pipelines, or scalable test systems Experience applying ML or AI techniques to systems problems — crash clustering, log anomaly detection, failure classification, or intelligent alert prioritization Familiarity with LLMs or generative AI tooling in an engineering context — prompt engineering, RAG pipelines ...

translating findings into practical actions. Using your expertise, you’ll design and deploy AI-driven security capabilities such as threat detection, anomaly detection and incident response. You’ll also advise on risk, compliance and regulation, ensuring we continue to meet industry standards as AI technologies ...

translating findings into practical actions. Using your expertise, you’ll design and deploy AI-driven security capabilities such as threat detection, anomaly detection and incident response. You’ll also advise on risk, compliance and regulation, ensuring we continue to meet industry standards as AI technologies ...

role has two halves that make each other work. You will build automated evaluation: model-assisted and LLM-based screening, agreement analysis at scale, anomaly and drift detection across annotation output. And you will build and run a dedicated audit team of around ten specialists in India … Build automated dataset quality evaluation and root-cause detection — model-assisted and LLM-as-judge screening, agreement analysis at scale, anomaly and drift detection across annotation output Hire, train, calibrate and manage a dedicated audit team of around ten specialists based in our India operation ...

scalability, and ease of maintenance. AI/ML Model Development Develop, train, and evaluate machine learning models that address core FinOps use cases: cost anomaly detection, spend forecasting, workload rightsizing recommendations, and commitment coverage optimization. Implement and maintain MLOps pipelines for model versioning, automated retraining, performance monitoring … drift detection as cloud usage patterns evolve. Experiment with and apply generative AI and LLM capabilities to FinOps workflows — such as natural language interfaces for cost querying, AI‐assisted tagging remediation, or intelligent cost allocation suggestions. Collaborate with FinOps analysts to validate model outputs against business expectations, refining ...

into production environments.### Technical Skills* Python and associated data science ecosystem (Pandas, NumPy, Scikit-learn).* Machine learning techniques including classification, regression, clustering and anomaly detection.* Experience with Generative AI, LLMs, RAG architectures or agent-based AI solutions.* SQL and large-scale data analysis.* Cloud-based data science platforms ...

known failure patterns, reducing human intervention and on-call burden by 50%+ Build comprehensive observability stacks (metrics, logs, traces) with ML-driven anomaly detection and AIOps capabilities Lead automated incident management: detection, triage, escalation, remediation, and post-incident review automation Drive DB automation: automated ...

through licence optimisation, hardware rightsizing, capacity rebalancing, decommissioning, and vendor renegotiationCost Governance Gaps — Assess and strengthen cost governance processes including approval workflows, budget controls, anomaly detection, and policy enforcementData Normalisation — Normalise cost data from disparate sources into a unified cost model, enabling consistent reporting across business units … months) based on value and feasibilityReporting & Dashboards — Design and recommend live/near-real-time cost dashboards with drill-down capabilities, trend analysis, anomaly alerts, and executive reporting viewsFINOPS PERSONA COLLABORATIONAs defined by FinOps.org, this role works with all key FinOps personas:Executives (CTO, CFO, CIO) — Present cost visibility ...

approach across development teams to improve monitoring consistency. Maturing from Monitoring to Full Observability Transition from basic alerting to proactive insights, leveraging AI-driven anomaly detection. Ensure comprehensive observability across frontend, backend, databases, cloud infrastructure, and networking. Implement Service Level Indicators (SLIs), Service Level Objectives (SLOs), and Error Budgets … Observability (AIOps) % of Issues Resolved via Automated Remediation – Reduce manual intervention in incident response. Reduction in On-Call Burden (%) – Minimize alerts requiring human intervention. Anomaly Detection Accuracy (%) – Improve proactive detection of performance issues. Business & Customer Impact Customer Experience Metrics (Latency, Errors, Uptime) – Ensure observability drives ...

communicating risk to executives while serving as a senior security advocate for sales, customer audits, and incident communications. You will own full-lifecycle detection and response (telemetry, automation, CI/CD detection-as-code, and MTTR/ATT&CK metrics) and serve as Incident Commander, running … team exercises and postmortem improvements. A major focus is driving AI security and internal AI governance: integrating LLMs/ML into SOC triage and anomaly detection, defending AI attack surfaces (agent activity, prompt injection, machine identities), hardening against AI-driven threats (phishing-resistant MFA, supply chain/ ...

ambitious collaboration between the public, charity and private sectors, designed to help people live healthier lives for longer through better prevention, earlier detection and improved treatment of diseases. We will speed up the discovery of new methods of early disease detection, and the evaluation … ideally genomic data. Developing models, algorithms or rule‐based systems that support human decision‐making, ideally where explainability, auditability and risk management are important. Anomaly detection, behavioural analytics, security monitoring or detection engineering. Risk quantification methods from fields such as actuarial science, epidemiology, operational research ...

platforms and AI workloads using metrics, logs, traces and telemetry. Establish operational insight models that support proactive decision-making and enable advanced capabilities including anomaly detection, event intelligence, noise reduction and predictive operational analytics.• AI Operations & Service Reliability: Apply reliability engineering principles to AI-enabled services, monitoring ...

Build and optimize monitoring, observability, and alerting capabilities using tools such as Prometheus, Grafana, AppDynamics, and Splunk. Apply AIOps capabilities to improve event correlation, anomaly detection, root cause analysis, predictive insights, and proactive issue prevention. Partner with engineering, infrastructure, production support, security, and risk teams to ensure ...

routing decisions, and reduce escalations. Advance AI-native analytics by leveraging Generative AI tools to automate recurring insight generation, support AI-powered summarisation and anomaly detection, and identify opportunities to replace manual workflows with intelligent automation. Enable experimentation and continuous improvement by supporting A/B testing ...

consumers Design feature stores and data pipelines that support AI/ML model training and inference for use cases such as FX pricing, payment anomaly detection, and limit utilisation forecasting Evaluate and integrate AI‐assisted data tooling (AI‐powered cataloguing, natural language querying, automated data quality) where ...

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. ...

OpenTelemetry traces, change events, and runbooks to accelerate Mean Time to Detection (MTTD) and Resolution (MTTR). Safe Production Automation: Develop proactive anomaly detection and Human-in-the-Loop (HITL) remediation workflows with rigorous safety, security, and quality guardrails. Reliability & Scalability Engineering: Partner with application ...

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. What … 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 ...

across data initiatives. Develop data quality, governance, metadata and data management frameworks. Explore how AI and Machine Learning can enhance data solutions through automation, anomaly detection and predictive data quality. Translate complex technical concepts and data insights into clear recommendations for senior stakeholders. Contribute to the development ...

chance to deliver real-world outcomes at scale. Key Responsibilities Designing, developing and optimising machine learning models across traditional ML use cases (forecasting, classification, anomaly detection) and GenAI/LLM solutions Leading experimentation cycles, including hypothesis definition, experimental design, evaluation and iteration, while complying with governance standards ...

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 ...

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 ...