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.

Trainees will learn Linux, Git, Docker, AWS, Azure, Google Cloud, CI/CD, and Infrastructure as Code. The program provides structured training and practical DevOps projects to build a portfolio. No prior experience is required.

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.

Wise★3.5 (42)· London

Lead a growing Solutions Engineering team in EMEA, providing technical consulting and product guidance to top-tier banks integrating Wise’s international finance products via REST API. Work with RESTful design, OpenAPI, OAuth2, and microservice architecture to architect integrations and influence the product roadmap.

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.

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.

You will architect Citi's Release on Demand platform, defining architecture, scalability, reliability, observability (tracing, logging, metrics), and E2E testing. The platform codifies governance and automates SDLC deployments for internal engineers. You'll lead a small, agile team distributed across Belfast, Canada, and London, mentoring engineers and owning the technical roadmap.

You'll work with Python (Pandas, Arrow, Spark) and potentially Lakehouse tools (Delta Lake, Databricks, AWS). Joining Global Data Engineering, you provide first-line support for a high-frequency trading market maker, ensuring robust data pipelines and APIs for trading teams.

You will work with Python, LLMs, agentic workflows, retrieval-augmented generation (RAG), and internal AI platforms. The Tactical AI Automations team deploys and supports AI tools across PIMCO's Portfolio Management and Analytics groups, including operational support for Lane Assist and POET (portfolio optimization tools).

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.

Data Idols★0.0· London

You will use Python, machine learning, recommender systems, LLMs, Generative AI, conversational

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.

Manage a team of test and DevOps engineers building CI/CD, Yocto-based embedded Linux, test automation, and cloud infrastructure (Python, C/C++, AWS/GCP/Azure) for non-invasive neuromonitoring medical devices.

Work with Azure, Terraform, Azure DevOps, container platforms, and AI agents to build and automate cloud infrastructure. You will design deployment pipelines, observability, and engineering tooling that enable rapid delivery of both AI-powered and traditional cloud-native applications.

You'll build scalable data infrastructure for AI applications using Python, SQL, Spark, dbt, LangChain, and cloud platforms (AWS, Azure, GCP), working with data pipelines, embeddings, vector search, and LLMOps tools like Terraform and LangSmith. QuantumBlack, McKinsey's AI division, develops next-generation AI systems and enterprise solutions for clients across industries. You'll collaborate with Data Scientists and ML Engineers in cross-functional Agile teams based in London, supporting the firm's AI adoption and complex problem-solving at scale.

Work with Kubernetes, Helm, Argo CD, GitOps, HashiCorp Vault, Prometheus, Grafana, Open Policy Agent, and Backstage to build and manage microservices configurations. The Webex Engineering Group owns the design and delivery of Kubernetes-based configurations for products like Webex Meetings, Webex Calling, and Webex Contact Center.

Design, code, and deliver Kubernetes-based microservices configurations using Kubernetes, Helm, and Argo CD within a GitOps model. You will use Prometheus, Grafana, Open Policy Agent, and HashiCorp Vault to ensure reliability, governance, and secure secrets management while advancing a self-service platform with Backstage. The team builds Webex Meetings, Calling, and Contact Center for the Webex Engineering Group.

JPMorgan★3.9 (20k)· London

You will work with Python, AWS, EKS, Docker, Kubernetes, Kafka, and MLOps tools. As a member of the Applied AI/ML Software Engineering team within CIB Security Services Technology, you will build cloud-based frameworks for hosting machine learning models alongside Data Scientists to integrate AIML solutions into production systems.