199 jobs for “Cloud”
PhysicsX London
Owns deployment of ML models and engineering surrogates (deep learning on CAE/CFD/FEA data, forecasting, anomaly detection) into customer production environments, building scalable training and inference pipelines with Python, PyTorch, Kubeflow and fastAPI. Leads scoping and architecture, mentors engineers, and travels to customer sites roughly 3-4 weeks per quarter.
PhysicsX New York, United States
Builds and automates high-fidelity electromagnetic models across low- and high-frequency applications, from chip and package design to EMI/EMC, using tools like HFSS, Maxwell and CST. Works at the intersection of CAE and data science, generating simulation data to train ML surrogates, collaborating with customers and mentoring junior engineers.
Faculty London
Builds and deploys production-grade ML software, tools and infrastructure, creating reusable solutions that accelerate ML delivery and capability testing of frontier AI models. Leads technical scoping and architectural decisions, advises clients and partners, and works with frontier labs on AI safety red teaming and evaluation.
Faculty London
Lead the design, build and deployment of production-grade ML and LLM systems for national security and AI safety clients, including capability testing of frontier models. Act as a trusted technical advisor, define engineering standards for ML at scale, and mentor junior engineers while working with frontier labs on red teaming and evaluation.
Medtronic London £76,800 – £115,200 pa
Builds and maintains a Python-based SaaS platform for project, data and annotation management used by internal machine learning engineers and data scientists. Works with data and platform engineers to handle growing data volumes, and collaborates with stakeholders to define requirements and validate features in an iterative, quality-driven environment.
Latent Labs London
Work at the interface between frontier generative models and pharmaceutical/biotech customers, deploying, adapting and fine-tuning models in customer environments. Build production-grade API integrations, model-serving infrastructure and ML data pipelines, while acting as technical point of contact for partners. Combines deep ML research with customer-facing delivery in an interdisciplinary team.
Latent Labs London
Customer-facing engineering role deploying frontier generative biology models into pharma and biotech environments, owning the full lifecycle from technical scoping to production delivery. Involves building API integrations, data pipelines and model-serving infrastructure on AWS with Docker and Kubernetes, plus on-site work with international partners.
Harnham - Data and Analytics Recruitment London £75,000 – £85,000 pa
Owns the infrastructure, deployment and operational excellence of production machine learning systems within a growing AI and data organisation serving the public sector. Builds ML orchestration pipelines with Dagster, Airflow or Prefect, runs Kubernetes and Terraform/Bicep infrastructure, and maintains CI/CD and observability for models and LLM-powered solutions, working closely with data scientists and client-facing teams.
VIQU IT Recruitment London £90,000 – £95,000 pa
Own the end-to-end architecture of an enterprise AI platform spanning Azure and AWS, covering ML training, model hosting, MLOps and Generative AI. Define patterns for LLMs, RAG, vector search and agent frameworks, produce reference architectures and standards, and provide technical direction to ML and platform engineers while embedding security, governance and regulatory requirements.
Tenth Revolution Group London £100,000 – £115,000 pa
Designs end-to-end AI and Generative AI architectures for enterprise transformation programmes, leveraging LLMs, RAG, vector databases and AI agent frameworks. Works with senior client stakeholders to translate business challenges into scalable, secure and compliant AI solutions and roadmaps, supporting deployments across Azure, AWS or GCP. Combines technical leadership with client engagement in a consultative consultancy environment.
Medtronic London £76,800 – £115,200 pa
Designs, builds and maintains AWS and on-prem infrastructure used by ML engineers, data scientists and product teams, with a strong focus on Kubernetes, Helm, Terraform/OpenTofu and GitOps delivery. Also handles platform service requests, incident diagnosis and reliability improvements, working across engineering teams in a surgical AI centre of excellence.
Darktrace Cambridge
Conducts independent and collaborative research across large language models, statistical methods and classical machine learning, prototyping ideas and implementing models into the wider software stack. Produces optimised solutions for latency and memory, including deployment on edge devices, working closely with software engineers and security analysts.
167 Solutions London £150,000 – £170,000 pa
Lead a multidisciplinary Applied AI and Data Science team while staying hands-on with Python, reviewing models and prototyping solutions. The role spans agentic AI, RAG pipelines, NLP and LLMOps, with architecture work across AWS Bedrock, SageMaker and Lambda, plus driving AI-native engineering with tools like Claude Code and Copilot.
Data Idols London £105,000 – £120,000 pa
Hands-on technical leadership role driving the design, development and deployment of AI, Machine Learning and Generative AI solutions, including LLMs and agentic AI. Partners with business and technology stakeholders to identify high-value use cases, prototype rapidly and take solutions through to production, while shaping the AI technical roadmap and engineering best practices.
Tenth Revolution Group London £100,000 pa
Embedded with business teams to understand operational challenges and build production-ready applications, AI solutions and automation tools. Works across Python, TypeScript, Java or C# on Azure, AWS or GCP, developing proof-of-concepts and scaling them with product, data and engineering teams. Acts as the bridge between technical teams and non-technical stakeholders, driving adoption of LLMs and agentic workflows.
