HSBC★4.0 (16k)· London

Work with Python, Docker, Kubernetes, Flask/FastAPI, PyTorch/TensorFlow, SQL Server/Postgres, and CI/CD pipelines to build and deploy production-grade ML and GenAI services. You will join the Growth AI programme within HSBC’s Corporate & Institutional Banking Data & Analytics, developing AI applications to identify business opportunities and mitigate client attrition.

You will use Python, KQL, SPL to build detection logic and automate response. You will protect Writer’s AI platform, training data, and model deployments, detecting threats like prompt injection and model extraction. You will coordinate incident response across teams and hunt for threats in GPU clusters and distributed training environments.

Infrastructure engineers at Writer use AWS, GCP, Azure, Kubernetes, Helm, Terraform, Python, and Go to build resilient, automated systems for the company’s enterprise AI platform. The role ensures 24/7 availability and reliability for customers like Mars and Marriott. The team reports to a director of engineering.

Manage infrastructure on AWS, GCP, Azure, or OCI using Kubernetes, Terraform, and CI/CD tools like Circle CI and GitHub Actions. The team owns the full deployment lifecycle of SGP in live customer environments, building interactive AI applications and enterprise SaaS products.

Lead AI engineers use Python, LangChain, vector stores, and LLMOps tooling to build and operate production generative AI applications like copilots and agentic workflows. They design RAG pipelines, own end-to-end development, and integrate with enterprise data systems and APIs. This player-coach role seeds and grows the AI Engineering function within GCG technology at Capital Group, a global investment firm.

You’ll use Python, LLMs, RAG pipelines, embeddings, vector storage, and cloud-native tools to build production generative AI applications—copilots, assistants, and agentic workflows—that improve Capital Group’s investment process.

You will work with cloud infrastructure (AWS, GCP, or Azure), infrastructure-as-code, and build security tooling and automation. As a Junior Security Engineer at Helsing, a defence AI company, you will join a team identifying and reducing security risks across cloud, applications, identity systems, and products, performing threat modelling, security reviews, and hands-on testing.

You will work across cloud infrastructure, applications, identity systems, and products, using technologies like AWS, GCP, or Azure with infrastructure-as-code, container security, and cloud identity patterns. Helsing is a defence AI company building high-assurance systems for democratic nations. You will identify, assess, and mitigate security risks across the full technology stack.

Microsoft★4.2 (9k)· London

You will work with Databricks, Microsoft Azure (Data, Infrastructure, AI) services, Azure Data Services, AI Services, and cloud-native technologies. The Global Customer Success Chief Architect’s Office drives adoption of Databricks solutions on the Microsoft platform, leading architecture reviews and designing enterprise-scale data, analytics, and AI solutions.

Work with Amazon Bedrock, AWS AgentCore, Python, TypeScript, and AI orchestration tools (RAG, prompt engineering, tool/function calling). Deploy and monitor production AI/ML or GenAI workloads on AWS serverless, event-driven infrastructure with CI/CD and IaC. The company builds AI agent solutions.

Work with Python/Go, Git, Jenkins, Maven, Kubernetes or OpenShift to design and enhance a GenAI prompt security platform that protects enterprise LLM applications from prompt injection and adversarial attacks.

You will work with JavaScript, TypeScript, and offensive security tools for penetration testing, reverse engineering, and vulnerability assessments. You join a specialist security team at a global leader in Computer Vision, AI, and biometric identity, acting as the bridge between Security and Engineering to embed security throughout web application development.

You will work with AI security platforms, DSPM, DLP, IAM, and cloud security tools. The role leads AI security technical motion for Cyera, a company that helps enterprises securely adopt AI by providing data security and AI trust infrastructure.

Design and develop AI agents using AWS AgentCore, Amazon Bedrock, Python, and TypeScript. Build backend services with AWS Lambda, API Gateway, Step Functions, DynamoDB, and IaC tools like Terraform or AWS CDK for enterprise multi-agent workflows and production monitoring.

Work with the Databricks Data Intelligence Platform, Spark, PySpark, Python, Java or Scala, and major public cloud platforms (AWS, GCP, Azure). As a pre-sales engineer, you will demonstrate how Databricks' unified data and AI platform helps customers solve complex data challenges, collaborating with Account Executives and Senior Solutions Architects.

This role focuses on designing, building, and operating enterprise AI agents using AWS AgentCore, Amazon Bedrock, Python, and TypeScript. You will implement RAG, tool calling, and multi-agent workflows, developing backend services with AWS Lambda, API Gateway, Step Functions, and DynamoDB. The position also involves establishing production AIOps with observability, monitoring, and CI/CD pipelines using Terraform or AWS CDK.

**AWS**, **enterprise integration patterns** (event-driven, messaging, APIs, batch, streaming), and legacy systems (core banking, LOS, LMS, mainframes). You'll design target solution architectures for how **Taktile’s** Agentic Decision Platform integrates into large financial institutions' existing landscapes, owning trade-offs like **security, compliance, latency, and data residency**. You act as a senior peer to customer enterprise architects and CTOs across both pre-sales and post-sales engagements.

You will develop AI agents using AWS AgentCore, Amazon Bedrock, Python, and TypeScript. You will build and operate enterprise multi-agent workflows with RAG, tool calling, serverless AWS services, and production AIOps. The role involves designing, deploying, and monitoring scalable AI architectures with CI/CD and Infrastructure as Code.

You'll work with Python, Go, Kubernetes, OpenShift, Git, Jenkins, and CI/CD to design and develop a GenAI security platform protecting LLM applications from prompt injection and adversarial attacks. The team builds secure, enterprise-grade AI platforms for a global technology consultancy.

Janus Henderson Investors★3.8 (62)· London

You will secure AI systems (LLMs, agents, platforms) and build security automation with Python, APIs, SOAR, and workflow engines. You will integrate security logs and perform prompt engineering, RAG, and agent-based tooling for the Office of the CISO at an asset management firm.

Lead Cloud Engineer role: work with Terraform, Kubernetes, Amazon Bedrock, AWS, and Infrastructure as Code; architect AI-native infrastructure including Vector Databases, RAG architectures, and production-grade Agentic Workflows. The role is with an AI-native consulting partner, delivering cloud, AI, and platform engineering projects for clients such as PayPal and adidas. You will guide and mentor a team of cloud engineers.

Build and operate a Kubernetes-native cloud platform for GPU inference workloads, using Python or Go. You'll lead technical designs, own areas like request routing and adaptive scheduling, and implement optimizations such as speculative decoding and KV-cache reuse, working with Prometheus, Grafana, and OpenTelemetry.

You’ll work with Python, AWS, LLMs, and AI agents to build shared platforms, harnesses, and guardrails that enable internal teams to use machine learning and generative AI safely and deterministically. As the team’s lead, you’ll define technical strategy for agent safety, evaluation frameworks, and governance.

Faculty★0.0 (16)· London

Python, language models, generative AI architectures, and Bayesian methods are core

Visa★3.9 (1.2k)· London

Work with Kong API gateway (Lua plugins), Java/Spring Boot, Datadog, Kubernetes, and GenAI tools on Visa’s Edge team, which owns the API Gateway and authentication layer—the critical path for all external API calls. This is a small, high-leverage team designing and operating systems to keep the platform available, secure, and easy for partners to integrate with.