You'll use Python, SQL, shell scripting, containerization, infrastructure-as-code tools, and Scala/Java or Python to build and maintain scalable data pipelines. You'll support a financial technology company's data infrastructure, contributing to AI/ML experiences and international applications. No team size is stated.

Design, build, and operate a Kubernetes-based MLOps platform using Helm, Terraform, Kubeflow, KServe, vLLM, and JupyterHub. The platform supports the full model lifecycle—experimentation, deployment, and scalable inference—for AI, ML, and LLM workloads in production.

You'll build and operate a Kubernetes-based AI/ML platform using Helm, Terraform, Kubeflow, KServe, vLLM, and JupyterHub. This role focuses on creating infrastructure, tooling, and guardrails so MLOps and data science teams can reliably deploy and serve models at scale.

You will work with AWS, Python, SQL, ETL/ELT pipelines, data warehousing, and APIs. You will own the data platform for a company developing AI and machine learning products that help enterprise clients make decisions. You will start as a senior individual contributor with the opportunity to build and lead a future data team.

Monzo★3.5 (108)· London

You will work with LLMs, autonomous agents, RAG, sequence-based architectures, Python, SQL, and Go, developing and deploying machine learning models in production. You’ll lead the design of a human-in-the-loop system that predicts, identifies, and routes customer problems to scale support for Monzo’s banking products across multiple geographies.

You will work with Python, AI coding agents (e.g., Claude Code, Codex), and potentially AWS, Kubernetes, and Temporal. Unitary builds Virtual Agents — AI-powered software workers that automate complex workflows end-to-end in customer tools, focusing on commercial insurance, marketplaces, and primary healthcare.

You will use TensorFlow or PyTorch, Python, and CV/graphics libraries, plus 3D engines like Unreal or Unity, to develop generative models for graphics and video. As part of the Visual Computing Group, you'll design neural networks and ML for PlayStation's game rendering and live

DeepL★3.8 (4)· London

You'll work with C# / .NET, RESTful APIs, distributed architectures, and optionally React/TypeScript, plus MCP, CLIs, OAuth, and AI coding tools. You join the API Enterprise team, which builds enterprise adoption features (usage insights, cost controls, RBAC, compliance, data residency) for DeepL's Language AI API platform.

Use deep learning frameworks, distributed computing, GPU-based training, and MLOps to build and deploy batch and real-time ML models. The Search & Recommendations team develops personalised recommendations, product ranking, and AI styling experiences for ASOS's online fashion platform, helping customers discover products.

You'll work with PyTorch, TensorFlow, XGBoost, GPUs, CI/CD and containerisation on the Search & Recommendations team, building production-grade ML systems for personalised recommendations, product ranking and AI-driven styling at ASOS, covering the full lifecycle from experimentation to deployment.

Senior Machine Learning Engineer at ASOS, building production ML systems for personalized outfit recommendations and style discovery. You'll work with deep learning frameworks like PyTorch/TensorFlow, distributed GPU infrastructure, and MLOps practices including CI/CD, containerization, and observability. Collaborate with ML Scientists on batch and real-time models serving millions of customers.

Native Kotlin and Jetpack Compose development on a consumer-facing Android app for in-store grocery trips, running on a known device inside live stores. Integrates on-device edge AI (ML Kit, TensorFlow Lite) and uses AI coding tools like Cursor or Claude Code. Reports to the Head of Android.

FDJ UNITED★4.0 (96)· London

You'll lead a DataOps/MLOps team building an ML platform and data operations capability for FDJ United's Sportsbook, working with AWS, Terraform/CloudFormation, Spark, Kafka, and CI/CD pipelines for end-to-end ML lifecycle management. The role involves establishing best practices, monitoring, and governance while collaborating with Data Engineering, ML Engineering, Quants, and Data Science teams to personalize customer experiences and manage risk.

Wipro★3.8 (17k)· London

You will work with GenAI, LLMs, RAG, agentic frameworks, cloud platforms, DevOps, MLOps, and data governance. As an AI Strategic Advisor, you will define enterprise AI strategy, lead integration of generative and agentic AI, and architect full lifecycle AI initiatives for a global technology consulting firm.

Microsoft★4.2 (9k)· London

Manage a team of Security Solution Engineers advising customers on Microsoft Security solutions including Defender XDR, Microsoft Sentinel, Microsoft Entra, and Microsoft Purview. The team supports customers in evaluating applications, recommending tailored solutions, and demonstrating technologies to win technical decisions.

GSMA★3.7 (45)· London

You will work with AI/ML, 6G, 3GPP SA1 standards, Integrated Sensing and Communication (ISAC), Agentic AI, multi-agent systems, and AI communication protocols. The 6G Task Force shapes the industry’s 6G and AI agenda by producing technical analyses and white papers that inform global standards.

A Senior AI Engineer at Stakemate will use agent frameworks (LangGraph, LlamaIndex, Semantic Kernel, ADK), RAG, Python, cloud services (Azure/GCP/AWS), and SQL to move AI from experiments to production. They will shape the platform and architecture for a live betting product at a fast-growing social sports betting startup.

You will work with agent frameworks (LangGraph, LlamaIndex, Semantic Kernel, ADK), RAG pipelines, vector stores, Python, SQL, and a major cloud (Azure AI Foundry, GCP, or AWS). You will take AI agents from proof-of-concept to production within Stakemate’s live sports betting product, setting the governance and standards for the company’s AI estate. Reporting to the CTO, you will shape the platform and architecture as the company scales.

Machine learning engineers at this tech-centric prop trading firm will work with Python tools and libraries, plus ML frameworks like JAX or TensorFlow, building training and inference infrastructure. The role focuses on enhancing research workflows and designing APIs for the growing ML team, applying techniques such as neural networks, random forests, and ensemble methods.

Use Python, PyTorch, TensorFlow, scikit-learn, LLMs, NLP, and SQL to build and deploy ML models for trade surveillance. You'll enhance analytics that detect market manipulation by analyzing trader behavior and order execution. Collaborate

You'll work across the full stack with TypeScript, React, and Python, building a continuous AI-auditing platform for safe and responsible AI deployment in HR tech. As an early product engineering hire at a 5-person startup (growing to 10 by mid-2026), you’ll own product features end-to-end reporting to the CTO.

Allianz★3.8 (3.4k)· London

Use catastrophe modelling platforms, Power BI, Excel, VBA, and SQL for portfolio steering and CAT analytics. The Marine team performs strategic portfolio monitoring, natural catastrophe and terrorism modelling, accumulation management, and reinsurance-related analytics to support underwriting.

Demonstrate and customize AbsorbLMS in technical demos, and build trial portals and integration use cases for prospects. You will collaborate with Sales and Account Executives to support Enterprise-level clients in a presales capacity, using Salesforce, SharePoint, Confluence and Zoom.

AXA★3.9 (2.9k)· London

Design and implement secure architectures for enterprise security and AI/ML initiatives across AXA, using Docker, Kubernetes, cloud security, API security, micro-services, MLOps/MLSecOps platforms, threat modeling, and SSDLC. Defines technical security standards and reference architectures aligning with frameworks like NIST AI RMF and ISO/IEC 42001. Reports to the Chief Security Officer.

Vodafone★3.6 (3.2k)· London

Work with Elastic Stack, Python, Kubernetes, Terraform, Ansible, CI/CD, Git, and Linux on the Cyber Security/Prevent team, supporting security platforms and delivering hands-on automation engineering to improve reliability and resilience across Vodafone Group and local markets.