426 to 450 of 579 MLOps Jobs in London

where appropriate to accelerate insight and delivery. Provide thought leadership and best practices across the data science lifecycle, from exploration to deployment, including scalable MLOps, model governance, and responsible AI implementation. Mentor and develop data professionals, fostering a culture of innovation, continuous learning. What We're Looking ForProven experience leading … core ML libraries (e.g., spaCy, NumPy, SciPy, Transformers).Understanding of big data and cloud technologies (Spark, Hadoop, Azure, AWS, or GCP).Deep expertise in MLOps principles, scalable model deployment, model governance, and the practical application of responsible AI within enterprise environments. Excellent communication and stakeholder management skills with the ability ...

including generative AI and agentic systems, on cloud infrastructure. You will own end-to‐end delivery from problem framing and experimental design through scalable MLOps, evaluation, governance, and integration with strategic systems. You will help set technical direction, establish reusable patterns, and ensure solutions are reliable, secure, and observable … reliability, incident management, and continuous improvement Strong Python software engineering skills, including modular object‐oriented design, testing, performance tuning, and debugging Working knowledge of MLOps and LLMOps and distributed systems, including training and serving patterns, batch versus real‐time architectures, feature stores, orchestration, and scalable data processing Ability to design ...

security risk assessments and integrate findings into governance processes.3. Secure AI/ML LifecycleIntegrate security into ML pipelines (MLSecOps) including CI/CD and MLOps frameworks.Define controls for:Secure dataset handling and lineageModel versioning and integrity validationAccess control and secrets managementEmbed automated security testing into model development pipelines.4. Data Protection … Computer Science, Cybersecurity, AI/ML, or related field.Preferred QualificationsCertifications such as:CISSP, CISM, CCSPCertified AI Security (e.g., CAISP or similar)Experience with:MLOps platforms (e.g., MLflow, Kubeflow)AI red teaming and adversarial testingKnowledge of secure coding and DevSecOps practices.Familiarity with Responsible AI principles and ethical AI frameworks.Key SkillsAI/ ...

assessments and integrate findings into governance processes. 3. Secure AI/ML Lifecycle Integrate security into ML pipelines (MLSecOps) including CI/CD and MLOps frameworks.Define controls for:Secure dataset handling and lineageModel versioning and integrity validationAccess control and secrets managementEmbed automated security testing into model development pipelines. 4. Data … Science, Cybersecurity, AI/ML, or related field. Preferred Qualifications Certifications such as:CISSP, CISM, CCSPCertified AI Security (e.g., CAISP or similar)Experience with:MLOps platforms (e.g., MLflow, Kubeflow)AI red teaming and adversarial testingKnowledge of secure coding and DevSecOps practices.Familiarity with Responsible AI principles and ethical AI frameworks. ...

including generative AI and agentic systems, on cloud infrastructure. You will own end-to-end delivery from problem framing and experimental design through scalable MLOps, evaluation, governance, and integration with strategic systems. You will help set technical direction, establish reusable patterns, and ensure solutions are reliable, secure, and observable … reliability, incident management, and continuous improvement Strong Python software engineering skills, including modular object-oriented design, testing, performance tuning, and debugging Working knowledge of MLOps and LLMOps and distributed systems, including training and serving patterns, batch versus real-time architectures, feature stores, orchestration, and scalable data processing Ability to design ...

including generative AI and agentic systems, on cloud infrastructure. You will own end-to-end delivery from problem framing and experimental design through scalable MLOps, evaluation, governance, and integration with strategic systems. You will help set technical direction, establish reusable patterns, and ensure solutions are reliable, secure, and observable … performance management, reliability, incident management, and continuous improvementStrong Python software engineering skills, including modular object-oriented design, testing, performance tuning, and debuggingWorking knowledge of MLOps and LLMOps and distributed systems, including training and serving patterns, batch versus real-time architectures, feature stores, orchestration, and scalable data processingAbility to design intrinsic ...

prescriptive models for key use cases such as demand forecasting, optimisation, and anomaly detection in infrastructure projects.Establish best practices for model lifecycle management, including MLOps, monitoring, and retraining, ensuring alignment with AECOM’s global standards.Extract actionable insights from complex datasets to inform strategic decisions across infrastructure, environmental, and urban development … urban development sectors.Strong proficiency in Python (pandas, scikit-learn, PyTorch/TensorFlow) and SQL, with experience in geospatial and environmental data analysis.Experience with MLOps tools (MLflow, Docker, CI/CD pipelines) and cloud platforms (Azure preferred), ensuring scalable and reliable solutions.Proven ability to influence non-technical stakeholders and communicate complex ...

lasting impact.At Credera, you will work on diverse client projects, such as building modern, AI-enabled data platforms on AWS using DevOps and MLOps practices, including large, distributed workloads, batch and streaming pipelines, and the data foundations that power machine learning, Generative AI and Large Language Model solutions. Working with … Azure DevOps, Jenkins, CircleCI etc.)Experience building LLM or foundation-model applications using frameworks such as LangChain, vector databases or agentic AI approachesExperience with MLOps and Infrastructure as Code (Terraform, CloudFormation etc.) for data, analytics and AI servicesExperience with data reporting and visualisation tools (Power BI, Tableau, Qlik etc.)Experience ...

test and deploy machine learning models, analytical pipelines and data science components that are robust, scalable and maintainable.Support the operationalisation of ML solutions through MLOps practices, including model monitoring, versioning, automated testing, CI/CD and performance optimisation.Apply responsible AI, model explainability, security, privacy and governance considerations throughout the development … PyTorch, TensorFlow or equivalent.Experience across the end-to-end ML lifecycle, including data preparation, feature engineering, model development, validation, deployment, monitoring and optimisation.Understanding of MLOps and production ML practices, including CI/CD, model versioning, automated testing, retraining, monitoring and reproducible delivery.Experience using cloud platforms and data science environments such ...

managing token, inference, and infrastructure spend so solutions stay cost effective as they scale. Integrate AI solutions with enterprise data systems, APIs, and MLOps/LLMOps tooling, applying sound system design and distributed-systems judgment. Apply responsible-AI judgment proportionate to the risk of each use case, working with risk … example pgvector, Pinecone, Weaviate, or Chroma) and agent or orchestration frameworks (for example LangChain, LlamaIndex, or the Model Context Protocol). Experience with MLOps/LLMOps tooling such as experiment tracking, model versioning, monitoring, evaluation/observability platforms, or CI/CD for ML and LLM systems. Experience implementing responsible ...

Woven by Toyota is enabling Toyota’s once-in-a-century transformation into a mobility company. Inspired by a legacy of innovating for the benefit of others, our mission is to challenge the current state ...

LondonProduct & Technology – AD/ADAS/Employee/HybridWoven by Toyota is enabling Toyota’s once-in-a-century transformation into a mobility company. Inspired by a legacy of innovating for the benefit of others ...

Job DescriptionRole - Senior Forward Deployed Engineer (AI‐Native) – Principal Technology Architect Technology - LLMs, agentic coding systems, intelligent IDEs, AI development platforms Location - LondonCompensation - Competitive (including bonus)Job DescriptionToday, the corporate landscape is dynamic and the ...

Title: MLOps Platform Developer Location: London Salary: Depending on experience Job Type: Full time, Permanent This is a rare opportunity to be first in the door to continue the development of our engineering platform. You will own a production system end to end the web application, the database estate … with the experience and relevant job titles of: Lead Full-Stack Engineer, AI Application Engineer, Applied AI Engineer, LLM Ops Specialist, AI Platform Engineer, MLOPS Engineer, Founding Engineer, Full-Stack AI Developer, AI Software Developer may also be considered for this role. ...

coaches and athletes at any level to capture video, analyze data, share highlights and more.Ready to join us Your RoleWe're hiring a Senior MLOps Engineer for our Hardware Group to build and scale the machine learning infrastructure that powers Focus, our line of smart cameras. … into optimised inference engines for devices like the Jetson Orin, building the "nervous system" for the next generation of automated sports capture.As a Senior MLOps Engineer, you'll:Build scalable Edge infrastructure. You'll design, develop, and maintain the delivery systems that enable us to deploy models to fleets ...

simulating the effect of modulating a target of interest. Breakthrough AI research only creates value when it can be reproduced, scaled, deployed and trusted. MLOps sits at the heart of that challenge. The AI Accelerator's ability to train biomedical foundation models, manage experimentation at scale, operationalise discoveries and deliver … quality AI depends on the ML platform, tooling and practices that underpin the entire model lifecycle. THE POSITIONWe are seeking a senior leader in MLOps Engineering to join Computational Innovation's AI Accelerator (@computationalinnovation). In this role, you will own the MLOps architecture, standards and technical direction ...

workflows for researchers and ML teams. You will collaborate with researchers, ML engineers and platform teams, implement IaC, ensure security and governance, and support MLOps pipelines from experimentation to deployment in a hybrid office setting (3 days in the office). #J-18808-Ljbffr ...

involves working collaboratively to define the company's 2026 technical roadmap while solving complex challenges in autonomous systems. The role requires expertise in AI, MLOps, Software Engineering, and Data Science. The company offers a range of benefits, including family-first relocation assistance and full UK visa sponsorship. #J-18808-Ljbffr ...

InfraPlat team, building and operating the infrastructure platform that powers our massive product catalog. You’ll drive multi-account/region Kubernetes clusters, MLOps, standardized deployments and the company-wide authentication platform. Collaborating with product and engineering, you’ll scope requests, identify shared pain points, mentor teammates, and help engineers ...

senior technical voice for the most sensitive AI decisions our clients face. Your work will define standards for responsible AI, model governance, and production MLOps, architect high-risk systems, and build reusable capabilities that enable multiple teams to ship safer AI at scale. #J-18808-Ljbffr ...

ensuring governance, safety, and human-in-the-loop decision-support for MoD stakeholders and prime contractors. Responsibilities include edge optimization, graph processing, AI governance, MLOps in low-bandwidth #J-18808-Ljbffr ...

Reliably fitting models with complex job dependency graphs Robustness in production, including validation and monitoring Qualifications An appreciation of good architecture and MLOPs best practice The ability to collaborate with, and influence, technical and non‐technical people A passion for end‐to‐end ownership of solutions, from articulation to delivery ...

Experience applying AI both to front-office decision-making and to how change and operations are delivered. Strong grasp of the modern AI ecosystem, MLOps, evaluation and monitoring, and responsible-AI practice. AI governance experience and the ability to make data fit for AI, working through certified data products ...

solutions from concept to production Develop applications using LLMs, Generative AI and Agentic AI frameworks Deliver predictive, optimisation and automation use cases Build robust MLOps and LLMOps processes, including monitoring and evaluation Work closely with engineers, data scientists, product teams and business stakeholders Help establish AI engineering standards, governance … software engineering background Commercial experience delivering machine learning models into production Hands-on experience with GenAI, LLMs and AI application development Knowledge of MLOps, LLMOps, CI/CD and containerisation Experience with cloud platforms, ideally Azure Understanding of model evaluation, monitoring and responsible AI Strong communication skills with the ability ...

infrastructure changes. Azure DevOps Pipelines : Design, build, and maintain secure, high‐speed CI/CD pipelines within Azure DevOps to automate code promotion and MLOps workflows. Incident Management : Lead the response, containment, and resolution of critical platform outages, serving as a primary escalation point for high‐priority cloud and infrastructure … requirements, post‐mortems, and engineering strategies effectively to both technical and non‐technical stakeholders. AI/ML Platform Automation : Architect and maintain highly available MLOps platforms across AWS and Azure environments. Developer Enablement : Build self‐service infrastructure tools, deployment templates, and continuous training/inference pipelines to eliminate friction. ...