Lead the EMEA Solutions Engineering team for Wise Platform, working with REST APIs (RESTful design, OpenAPI, OAuth2) and microservices architecture. The team helps top-tier banks and tech companies integrate Wise’s international finance products via their public API, providing post-sale technical consulting. You will build and mentor a high-performing technical delivery team, defining goals aligned with Wise Platform’s objectives.

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.

Work with Azure OpenAI, AI Foundry, Databricks Mosaic AI, MLflow, Genie, AgentBricks, LangChain, LangGraph, AutoGen, Delta Lake, DLT, Unity Catalog, Python and SQL. You will design, build and deploy GenAI, RAG and Agentic AI solutions from prototype to production as part of a major AI and data transformation programme across a global energy organisation’s trading business.

Design, build, and deploy quantitative models using Python, R, and libraries (numpy, pandas, scikit, pytorch, tidyverse, caret, ggplot) plus NLP tools, SQL, AWS, Sagemaker, DataBricks, Snowflake, MLFlow, and Weights & Biases. The team creates systematic country risk analytics and ML/NLP pipelines for BMI’s market insights, supporting clients in risk assessment.

Wise★3.5 (42)· London

Build backend

Databricks★3.0 (21)· London

You will work with Python, Scala, JavaScript/TypeScript, Apache Spark, and cloud ecosystems (AWS, Azure, GCP) to build production-grade data and AI solutions on the Databricks platform. In this hands-on role, you lead architecture and design decisions while embedding with customer teams to deliver end-to-end data pipelines, ML/AI models, and user-facing applications.

Fitch Solutions★3.0 (2)· London

As a Data Scientist at BMI (a Fitch Solutions business), you will design, build, and deploy quantitative models using Python and R (e.g., numpy, pandas, scikit, pytorch) to power advanced analytics. You will develop and maintain ML/NLP and data pipelines, working with Economists and Analysts to build risk models and generate insights from structured and unstructured data.

You will work with Azure and AWS, Terraform, Azure DevOps, CI/CD, GitOps, GitFlow, Kubernetes, Docker, and MLOps tooling. This senior engineering role evolves a modern cloud platform, building Infrastructure as Code and automation to enable software, data, and machine learning teams to deliver at scale.

You will work with Java, JavaScript, Python, or C#; open-source frameworks like Cypress, Karate, PlayWright, or Selenium; CI/CD pipelines in Azure DevOps; and JMeter for performance testing. As part of an agile team, you’ll help clients achieve business transformation goals through automated and manual testing.

You will work hands-on with Java, Kafka, Kubernetes/OpenShift, caching solutions, REST APIs, Spring Boot, SQL, Git, and Gradle/Maven within a high-volume message processing environment for a trading client in the financial services domain.

Work with Python, PyTorch, scipy, scikit-learn, numpy, pandas, MLflow, Docker, Kubernetes, Ray, and GCP to build and scale distributed compute for research workloads. Phaidra creates AI-powered control systems for the industrial sector using reinforcement learning to enable facilities to learn and improve automatically.

Lead a team building enterprise-scale AI platforms for a top-tier hedge fund, using Python, C#, Scala, Java or Go; LangChain, LlamaIndex, Claude Code, Aider; MLflow; SQL, Pandas, Polars, Snowflake, Vector Databases; Docker, Kubernetes, Helm, Flux; and Airflow, Dagster, DBT. You'll architect multi-agent systems, orchestration frameworks, and production AI applications to transform investment workflows.

EPAM Systems★3.8 (703)· London

You will work with Python, SQL, GenAI frameworks (LLMs, RAG, LangChain, Hugging Face), cloud-native AI services (AWS, Azure, GCP), and MLOps tools such as Databricks or MLflow. As a consultant for EPAM, you will help clients shape and implement data-driven solutions, designing predictive models and GenAI-based applications to drive enterprise AI transformations.

AXIS Capital★5.0 (1)· London

You will work with Python, Azure AI services (Azure OpenAI, Azure AI Search), Git, LangChain, Semantic Kernel, and MLOps tools for model versioning, deployment, and monitoring. As deputy to the Head of Data Science & AI Delivery, you lead the day-to-day execution of AI delivery across strategic projects and use cases for AXIS Capital, a specialty insurance and reinsurance provider. You directly manage and contribute hands-on to technical design, code reviews, and solution delivery for a growing team of Data Scientists and AI Engineers.

You'll lead a 4–5 person team building machine learning systems using Python, TensorFlow, PyTorch, JAX, AWS, Docker, and Kubernetes for Super's Playstack platform, which powers sports betting and gaming experiences across Europe and Latin America. The role combines technical contribution, project management, and people leadership, focusing on predictive analytics, personalization, fraud detection, and real-time recommendations that enhance customer engagement in online gambling and sports betting.

Netbuilder★3.0 (1)· London

Full-stack developers use Java, Spring Boot, React, TypeScript, Oracle, MySQL, Git, Jenkins, Spinnaker, Docker, and Kubernetes to build, enhance, and support scalable web applications, APIs, and enterprise platforms for NETbuilder, a digital solutions provider.

Develops and maintains Java-based full-stack applications. Works with AWS cloud services, Kafka for messaging, and containerisation tools such as Docker. Versions 1 delivers technology solutions for global enterprise clients.

Python, SQL, PyTorch/TensorFlow, Hugging Face, LangChain, MLflow, Docker, Kubernetes, AWS/Azure/GCP, Databricks, Snowflake. You will build, deploy, and optimize ML and Generative AI solutions, including RAG systems, AI agents, and LLM applications.

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.

Work with Python and common data science libraries, modern AI concepts including large language models and retrieval-based approaches, and tools such as GitHub Copilot, Databricks or MLflow. You will join a consulting team combining data science, AI and strategic problem-solving to deliver rapid insights, prototypes and analytical solutions across multiple industries.

HeliosX★3.5 (19)· London

You'll work with Snowflake, Hex, Claude, and MLFlow or SageMaker in Python to build and deploy clinical prediction, risk stratification, and treatment-optimization models for a digital healthcare platform that personalizes patient journeys.

You will design and maintain automated UI, API, and service-level tests in TypeScript using Serenity/JS and Playwright, plus Python/pytest and C#/.NET. The role involves testing the ACE application and internal platform tooling for a decentralised AI platform optimising energy in data centres.

The role involves Python, FastAPI, Docker, Kubernetes, CI/CD, MLOps tools (MLflow, Weights & Biases), and workflow orchestration (Airflow, Prefect, Kubeflow) for deploying and monitoring ML models in production. You'll build and maintain ML infrastructure, pipelines, and observability for an edge computing environment at a company scaling its ML platform.

Lead Engineer using Vue (frontend), Django/Laravel (backend), AWS, Stripe/Salesforce/Netsuite integrations, SOAP, REST/OpenAPI, and project tools like JIRA, GitHub. Builds and maintains the user-facing commercial platform central to customer interaction. Manages and mentors junior developers, acting as project manager for releases.

Lead a team of 5+ engineers owning end-to-end ML infrastructure, using Python, MLflow, AWS SageMaker, React, Docker, Spark and Kafka. You will drive the migration from MLflow to AWS SageMaker, building scalable ML systems for predictive maintenance and fault detection, while delivering React-based frontends.