Java, integration patterns, CI/CD, observability, and cloud-native services. Leads a team building and operating reusable build kits, deployment automation, and common integration platform services. Manages and coaches a multi-skilled engineering team.

You will work with Java microservices, Spring Boot, Python, SQL, Snowflake, dbt, Airflow, LangChain, Sagemaker, and MLflow. The data science team builds ML solutions for risk-based decisioning, pricing, and operational automation in insurtech/fintech. This is a hands-on leadership role—you will lead and develop the team while setting technical standards.

Sainsbury’s★3.5 (16k)· London

As Engineering Manager in Integration, you'll lead and coach a multi-skilled team building reusable integration kits, common components, and platform services using JAVA. You'll drive CI/CD, observability, and cloud-native services to standardise how integrations are built, deployed, and operated across Sainsbury’s.

Deloitte★3.9 (14k)· London

You will work with Python, NumPy, Pandas, scikit-learn, LangChain, Hugging Face, LlamaIndex, Azure Machine Learning, Foundry, Git, and Agile practices. The team is Deloitte’s Tax Digital Innovation AI & Data Studio, which researches, builds, and delivers analytics and AI-driven solutions for tax and legal clients.

Leading multiple agile teams delivering enterprise web and mobile platforms, the manager drives technical strategy and AI-driven innovation. The role requires expertise in React, React Native, Node.js, CI/CD, and DevOps, alongside applying SAFe frameworks for scaled agile delivery.

Lead a disputes team at Checkout.com, working with a .NET/C# back end, React/TypeScript front end, and Python for machine learning, alongside REST APIs, event-driven microservices, and SQL. The team builds tools and models to help merchants manage and win chargeback disputes at scale. You will oversee engineers across disciplines and seniority levels, driving predictable delivery and operational efficiency.

Lead a team of DataOps and MLOps engineers building and evolving the ML platform and data operations for FDJ United’s Sportsbook product. You’ll work with AWS, Terraform, CloudFormation, Spark, Kafka, and CI/CD pipelines to enable end-to-end machine learning workflows from research to production.

Lead a team of ML engineers building production systems—real-time inference APIs, batch data products, and LLM-based agentic solutions—for Compare the Market’s AI platform. You’ll set technical direction using Python, Terraform, GitHub Actions, ArgoCD, and frameworks like LangGraph/LangChain, ensuring robust, scalable, and observable systems.

Lead the Integrations team connecting Yuno’s payment platform to PSPs and gateways globally. You’ll work with Java, Golang, Kotlin, SQL, microservices, event-driven architectures, APIs, and AI-assisted coding tools like Claude Code, shaping scalable, secure payment infrastructure.

Checkout.com★3.8 (15)· London

Senior Engineering Manager leading the Disputes team, which builds tools, services, and machine learning models to help merchants manage and win chargeback disputes at scale. The team owns a full stack centered on .NET / C#, React and TypeScript front ends, Python-based ML, and event-driven microservice architectures.

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.

You will work with Python, SQL, PostgreSQL, Snowflake, Kafka, Airflow, Django/Flask/FastAPI, and practice TDD, BDD, and pair programming. As a principal individual contributor, you build and expand the data pipeline and event streaming infrastructure powering Europe’s largest equities exchange. You mentor engineers across Europe and the US within a small, highly skilled team.

Lead 6-7 engineers on core ML infrastructure for a systematic quant fund: distributed model training, LLM hosting/fine-tuning, and scalable deployment. Use Python with working knowledge of C++/Java. Own technical direction for real-world, high-stakes systems.

Manage the SAIL platform built with AWS, Kubernetes, Python, and infrastructure-as-code tools, enabling clients to control and leverage data through ML solutions embedded into business processes. Lead and mentor a squad of 4-6 engineers.

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.

You will work with Java, Kotlin, and Android OS to develop and maintain display and power frameworks for the Android ecosystem. As a Tech Lead Manager, you manage a team of software engineers, ensuring stability and extensibility of critical frameworks across devices.

You will lead test strategies across complex programmes, overseeing functional, performance, and automation testing teams. You will work with technologies including JavaScript-based testing tools, microservices, containerization, and DevOps practices. This role is at an AI enablement consultancy that delivers quality-led foundations.

You will manage large software engineering teams using Java, JavaScript, and cloud platforms like Azure or GCP. The company builds and maintains high-scale, consumer-facing technology products with both frontend and backend components. The role leads engineering transformation initiatives and oversees distributed systems.

Deloitte★3.9 (14k)· London

Lead client engagements designing and implementing advanced analytics and AI-driven Revenue Growth Management solutions, using Visualfabriq for TPO/TPM. You will advise consumer/healthcare clients on AI, machine learning, and predictive analytics to enhance commercial strategy. This role leads matrix project teams including offshore/nearshore resources.

This is a partnerships and business-development role, not a hands-on engineering position. You will work directly with founders, ML researchers, and scientists to create a global data-acquisition strategy for the company’s AI biology models. You will identify, negotiate, and secure complex data partnerships with health systems, biobanks, academic groups, and pharmaceutical companies.

Intercom★3.5 (4)· London

You will lead product engineering teams building Fin’s Customer Agent, a single AI agent that works across support

Flo Health★4.8 (26)· London

Lead a team of 6+ ML and backend engineers building production ML systems with TensorFlow, scikit-learn, CatBoost, and MLOps. You'll own Flo’s predictive lifetime value (pLTV) models, stand up Marketing Mix Modeling (MMM), and develop real-time campaign optimization algorithms to drive user acquisition strategy for the world’s #1 health and fitness app.

Google★4.3 (6.2k)· London

Work with Java and Kotlin on Android display and power management frameworks across devices. Manage engineers as a Tech Lead Manager, ensuring stability and extensibility of critical display and power frameworks for the Android ecosystem.

Engineering Managers at Ki lead teams working with Java, Python, JavaScript, Node.js, Spring Boot, Flask, Kotlin, GCP, and CI/CD tooling. Ki is the biggest global algorithmic insurance carrier, using machine learning and LLMs on its broker platform to digitise Lloyd's of London underwriting. The role collaborates with the CTO on technical strategy for the architecture, tech stack and infrastructure.

This senior director will shape marketing data strategy, working with first-party data, machine learning, marketing technology, AI, and analytics platforms. They join the Data & Analytics organisation at Forbes Advisor to build products and intelligence that power marketing and revenue growth. They will lead strategic initiatives across marketing measurement, customer intelligence, and data products, acting as a key technical counterpart to the VP of Data Strategy.