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.

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.

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.

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.

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.

Senior Data Engineer at Softwire, a UK digital consultancy, working with Python, SQL, Databricks, Snowflake, Fabric, and AWS/Azure/GCP across data architecture, pipeline design, and governance. You'll lead complex data engineering projects for clients across public and private sectors—from healthcare research platforms to energy market infrastructure—while mentoring junior engineers and shaping technical direction on multi-stakeholder engagements.

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.

Work with Java, GitHub Copilot, Devin.AI, Citi Squad, Stylus Workspaces (Gemini, OpenAI, Claude, Llama), Docker, Kubernetes, and CI/CD. Build high-performance, real-time FX risk management systems for a greenfield platform with a 10+ year strategic commitment. As a VP, lead end-to-end delivery and technical direction.

Design, build and deploy production Generative AI applications, LLM-powered solutions and agentic frameworks using Python, LangChain, LangGraph, and OpenShift. You will automate enterprise workflows with intelligent agents, working with PostgreSQL and pgvector as part of Citi’s COO-Technology team building scalable AI for global operations. This is an Individual Contributor Staff Engineer role.

You will build AI-powered products and platforms using Golang, Kubernetes, and cloud-native microservices. This role is on Citi’s AI Product & Platform team within the Chief Technology Office, creating scalable backend services and APIs for enterprise AI applications.

S&P Global★3.9 (1.7k)· London

Lead the AI+ strategy for S&P Global's Finance team, using AI/ML, NLP, and GenAI to improve finance processes and operational efficiency. This role will develop and execute the roadmap for automation and robotics, partnering with Corporate Finance and divisional CFOs. The team is within the Enterprise Finance and Strategy group.

You will use Salesforce and conduct multi-threaded outreach via phone, email, and social channels. Decagon builds a conversational AI platform whose AI agents power personalized customer interactions across voice, chat, email, and SMS.

EPAM Systems★3.8 (703)· London

You will work with AI, GenAI, cloud platforms (AWS, Azure, Google Cloud), mobile technologies, digital commerce, and omnichannel architectures. You will lead technology consulting engagements for retail and CPG clients, shaping digital strategies, defining enterprise and solution architectures, and driving AI-driven transformation roadmaps. The first engagement requires mobile expertise.

Bloomberg★3.8 (1.1k)· London

Work with Bloomberg Terminal data, Python, visualization tools (Power BI, Tableau, Qlik Sense), generative AI, machine learning, automation, and advanced Excel. Support the Technology equity research team by building financial models and conducting investment research on AI companies including Alphabet and Meta.

Google★4.3 (6.2k)· London

Google Cloud AI offerings (Vertex AI, Gemini), modern multi-agent systems (LangGraph, CrewAI). This role drives Gemini Enterprise adoption by industrializing AI excellence, moving licenses to activated, and ensuring production-grade deployment of AI agents and workflows. It leads the GE Accelerator efforts.

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.