Design and build production-grade ML systems using MCP servers, recommendation systems, RAG pipelines, LLMs, and foundation models with Python, TensorFlow, PyTorch, FastAPI, and LangChain. You will own the end-to-end AI lifecycle for a sales data platform, deploying models into batch/real-time environments to drive forecasting, segmentation, and early warning systems. This role focuses on embedding context-aware intelligence into decision-making tools for business stakeholders.

DNEG★3.1 (36)· London

Design, build, and maintain high-performance RESTful APIs, microservices, and event-driven architectures using Python, PostgreSQL, and Redis. You will integrate generative AI models (ATMAN, VAANI) into production backends for a company operating at the intersection of enterprise Media Asset Management and generative media.

Backend engineer using Java (preferred) or similar languages, Kafka or RabbitMQ, and SQL to build fraud detection and prevention systems for Wise's Receive Risk team. You'll support acquiring products like QuickPay and payment links, balancing seamless user experiences with strong fraud barriers across large-scale payment infrastructure.

You will architect and scale backend services using Python, FastAPI, and PostgreSQL, and work with message brokers like Azure Service Bus, RabbitMQ, or Kafka, plus Celery, Docker, and Kubernetes. The company builds an AI workspace processing sensitive financial data for large banks and regulated institutions under SOC 2, GDPR, and FCA compliance. You will report directly to the founders on a senior, staff-track IC team.

Design and build production-grade ML systems including MCP servers, recommendation engines, and RAG pipelines. Work with Python, LLMs, LangChain, FastAPI, PyTorch, Spark, SQL, vector databases, and AWS/GCP on a sales data platform to create chatbots, semantic search, and forecasting models. Own the full AI/ML lifecycle—from problem definition to deployment and monitoring—to drive sales strategy and decision-making.

Wise★3.5 (42)· London

You'll use Java, Kafka or RabbitMQ, SQL, and microservices on the Acquiring Risk team. The team builds fraud detection and prevention systems for Wise's acquiring products (QuickPay, Payment links, Invoices, e-commerce). You'll build a fraud engine to enable fast product shipping while minimizing risk and cost.

LMAX Group★0.0· London

You'll work with Python, Terraform, Ansible, Kubernetes, Airflow, Meltano, DBT, Kafka, Redpanda, RabbitMQ, MySQL, Snowflake, Iceberg, and AWS to build high-performance data pipelines and real-time streaming systems supporting Business Intelligence at LMAX Group, a Forex and digital currency trading firm.

Wise★3.5 (42)· London

You will work with Java 8+, Spring framework, and asynchronous message queues such as Kafka or RabbitMQ to build highly scalable microservices. This role is on the Global KYC and Onboarding squad, which builds and owns the verification lifecycle—from document collection to compliance monitoring—for Wise's personal and business customers.

You will work with Python, C# .NET, Snowflake SQL, AWS (EC2, S3), Airflow/MWAA, Kubernetes/EKS, and messaging tools (ZMQ, RabbitMQ, Kafka). The Orchestration team builds and operates a high‑scalable, AWS cloud-based risk platform that generates risk reports for tens of thousands of cash and derivative trades. You will design and support Python risk engines using Snowflake databases.

Nebius★0.0· London

You'll build web-scale crawling and content acquisition infrastructure using Go or C++ for Nebius's agent-native search platform. The role involves designing distributed systems to discover, fetch, and refresh billions of URLs from the open web, managing scheduling, deduplication, and ingestion workflows while ensuring high throughput, reliability, and content quality for AI agents at internet scale.

You'll work with object-oriented languages (C#, Java, Python, Go, etc.), cloud infrastructure (AWS), Kubernetes, CI/CD pipelines, messaging systems (RabbitMQ, Pulsar), and modern front-end technologies like React. Genius Sports builds next-generation sports betting technology, including a leading Sportsbook and Risk Management platform that uses advanced algorithms to help operators manage liability, optimize margins, and improve customer experience. You'll join a multi-disciplinary Agile team delivering highly distributed, real-time systems with measurable business impact.

CrowdStrike★3.8 (54)· London

You'll design and develop highly scalable streaming microservices using Apache Kafka, Apache Flink, Apache Spark, and Apache Storm in Java, Python, and Go, deploying on Kubernetes across AWS, GCP, OCI, and on-premises infrastructure. CrowdStrike's Real-Time Streaming Platform team processes trillions of security events daily, enabling threat detection and incident response at petabyte scale. The role requires 8+ years of software engineering experience with deep distributed systems and stream processing expertise.

Depop★4.4 (61)· London

You'll work with Python, PyTorch, TensorFlow, scikit-learn, SageMaker, MLflow, TFServing, Spark, Databricks, AWS services, Kafka, Airflow, and CI/CD tooling to build and deploy learning-to-rank models powering personalized ranking across Depop's circular fashion marketplace app. The Ranking team develops systems that order items for millions of users daily through search results and recommendations, requiring you to design training pipelines, optimize real-time serving, and maintain production infrastructure alongside ML Scientists, Backend Engineers, and MLOps specialists.

Genius Sports★4.4 (37)· London

You'll work with object-oriented languages (C#, Java, Python, Golang, etc.), microservices architecture, Kubernetes, AWS, CI/CD pipelines, messaging systems (RabbitMQ, Pulsar), and testing frameworks to build highly distributed, real-time systems for Genius Sports' Advanced Risk team. This team delivers risk management and Sportsbook products that help betting operators manage liability, optimize margins, and scale operations using smart algorithms and data-driven decision-making. You'll collaborate with software engineers, data scientists, QA engineers, and a product manager in an Agile squad model, leading multi-sprint features with measurable business impact.

You'll architect and evolve high-performance data infrastructure processing 26 billion monthly requests using distributed systems patterns, REST APIs, WebSockets, and message queues like Kafka, while working with databases such as Redis and PostgreSQL. The CoinDesk Data Engineering team builds market data systems powering crypto indices used in major ETFs and real-time analytics for institutions. You'll mentor senior engineers, lead cross-team technical initiatives, and report to the Director of Engineering.

You'll work with Java, Python, Clojure, Kubernetes, Docker, Kafka, RabbitMQ, Airflow, and SQL on DRW's Unified Platform—a consolidated trading technology that processes real-time trade and order feeds and manages the firm's investment book of record across equities, fixed income, FX, commodities, and other asset classes. You'll design microservices, handle both legacy and greenfield development, and focus on testing, reliability, and scalability while providing on-call support.

Wise★3.5 (42)· London

You'll work with Java, microservices, Kafka or RabbitMQ, and SQL databases to build a fraud detection and prevention engine for Wise's Acquiring products (QuickPay, Payment links, Invoices, e-commerce). The Receive Risk team within the Global Product tribe focuses on enabling customers to receive money securely while blocking fraudsters, balancing seamless user experiences with strong fraud barriers.