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).

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.

Work with Python, Pandas, NumPy, Scikit-learn, SQL, LLMs, RAG, and MLOps to design, build, and deploy enterprise-scale AI/ML solutions improving operational efficiency, service resilience, and customer outcomes at LSEG, a global financial markets infrastructure and data business.

LSEG★3.7 (402)· London

You will use Python (Pandas, NumPy, Scikit-learn), SQL, cloud-based MLOps platforms, and Generative AI/LLM/RAG tools to build predictive models and AI solutions that improve operational efficiency, service resilience, and customer outcomes for LSEG's financial markets infrastructure.

You will design, build, and operate AWS infrastructure, engineer Kubernetes environments (EKS, Docker, Helm), and use Terraform for infrastructure-as-code. You will support critical investment and trading systems, improve CI/CD pipelines with GitHub Actions and GitOps (Argo CD), and work with databases like Snowflake, Aurora, and SQL Server. You will take end-to-end ownership of production systems, solve complex problems across distributed environments, and influence cloud architecture and reliability standards.

Wise★3.5 (42)· London

Build backend

Apple★4.1 (14k)· London

You'll work with Go, Python, Linux, and distributed systems including object stores, databases, and data pipelines on private/hybrid cloud. The team builds and operates the data platform infrastructure behind Apple’s services (iCloud, iTunes, Siri, Maps), keeping petabyte-scale workloads reliable and performant.

Automat-it★0.0· London

You will work with AWS data services (S3, Glue, Redshift, Kinesis, MSK, EMR, Athena, Lambda, IAM) and modern tooling like Iceberg, dbt, Kubernetes, Docker, Terraform, and CI/CD. As a Senior Data Architect, you design and deliver end-to-end production-grade data platforms on AWS directly for startup customers. Automat-it is an AWS Premier Partner and Managed Services Provider.

Lead Cloud Engineer role: work with Terraform, Kubernetes, Amazon Bedrock, AWS, and Infrastructure as Code; architect AI-native infrastructure including Vector Databases, RAG architectures, and production-grade Agentic Workflows. The role is with an AI-native consulting partner, delivering cloud, AI, and platform engineering projects for clients such as PayPal and adidas. You will guide and mentor a team of cloud engineers.

The Senior Data Practice Lead works with SQL, Python, data modelling, transformation architecture, pipeline design, data testing, observability, CI/CD, and data governance. At YLD, a software consultancy embedding engineers in client teams, this role builds the data practice through hands-on delivery, client engagement, and mentoring. Reports to Head of Engineering.

Multi-cloud platform engineer (senior/lead) for Defaqto, working with AWS, Azure, Google Cloud, Terraform/OpenTofu, Pulumi, Ansible, Docker, Kubernetes, Python, Go, GitOps, CI/CD, BigQuery and AI/LLM tooling. Builds and operates the company’s hybrid-cloud internal developer platform and self-service golden paths for human engineers and AI agents.

As a Member of Technical Staff on the Data Platform team, you'll use Spark, Flink, Beam, Airflow, Dagster, Kafka, PubSub, Parquet, Iceberg, Delta Lake, BigQuery, Snowflake, and Great Expectations to build and operate core data systems and pipelines. These power Reflection's research, training, and production environments, enabling high-velocity experimentation and scalable model development.

Will work with KDB+/q, real-time data ingestion, historical databases, and Linux/Unix scripting to build platforms for Commodities and Global Markets, supporting Quantitative Investment Strategies with market data for trading, risk, and research across multi-asset products including commodities, options, futures, and indices. Joins a small, hands-on team modernising the KDB ecosystem with cloud migration and AI.

Design backend services using Python, FastAPI, Node.js, Docker, Kubernetes, and cloud platforms (AWS/GCP) with SQL/NoSQL databases. You will build and evolve OneSuite, a unified marketing intelligence and automation platform powering agentic workflows, data processing, and channel automation. This is an individual contributor role.

Use Terraform, Kubernetes, GitHub Actions, AWS Bedrock, Kinesis, Kafka, dbt, and Airflow to build self-service developer platforms and enterprise AI infrastructure. As a Senior Cloud & AI Platform Engineer at an AI-native consulting firm, you will implement data pipelines, agentic workflows, and observability pipelines for clients like PayPal and adidas.

Work with Terraform, Kubernetes, Vector Databases, Retrieval-Augmented Generation (RAG), Agentic workflows, OpenTelemetry, and Infrastructure as Code. As a consulting partner for clients like PayPal and adidas, you lead cloud-to-AI platform engineering, building autonomous platforms and managing AI lifecycles. You also mentor a team of cloud engineers.

LSEG★3.7 (402)· London

You will work with Java, Python, C++, C#, or similar, plus AWS or Azure, CI/CD, and AI-assisted development tooling. The team modernises and scales FTSE Russell’s cloud-native, low-latency platform that powers global benchmarks and index solutions for investment decisions.

LSEG★3.7 (402)· London

You'll work with Java, Python, C++, C#, AWS or Azure, CI/CD pipelines, and AI-assisted development tools to modernise and scale FTSE Russell's cloud-native, low-latency index platform that underpins global investment products.

Worldpay★3.0 (53)· London

You will work with Python, AWS, CI/CD pipelines, and ML tools like MLFlow, FastAPI, and containerization. As part of the Data Science Enablement team, you will own the technical delivery of production ML products and contribute to the shared ML platform for Worldpay’s payment solutions.

Design and scale infrastructure for CoreWeave's GPU Intelligence Platform using Python, Go, Rust, or C++. Build data ingestion, feature pipelines, and deploy ML models on Kubernetes with streaming tools like Kafka. The team develops a reliability platform for fleet-level anomaly detection, prediction, and automated root cause analysis.

You will work with batch and streaming pipelines, CI/CD, infra-as-code, and data observability tooling (metrics, logs, traces, data-quality) to implement end-to-end data lineage. The Monolith AI Platform Engineering Team builds the data and workflow backbone powering engineering simulation and AI workflows.

LSEG★3.7 (402)· London

You'll design and scale data distribution systems delivering index data via APIs and cloud platforms, working with TypeScript, Python, SQL, AWS (Lambda, API Gateway, S3, Aurora), PostgreSQL, Apache Iceberg, Terraform, and GitLab. FTSE Russell (LSEG) builds next-generation distribution capabilities for institutional clients worldwide, requiring you to own system architecture, data modeling, and engineering standards as a hands-on senior engineer with strong technical leadership expectations.

EPAM Systems★3.8 (703)· London

You'll work with Python, LLMs, and multi-agent orchestration frameworks including LangGraph, CrewAI, and AutoGen to design and deploy Generative AI and Agentic AI solutions. The role involves building RAG pipelines, implementing multi-step agent workflows, and developing production-grade microservices integrated with enterprise systems across multiple industries. You'll apply observability tools like Langfuse and Grafana while working within EPAM's Data & AI Practice, which delivers AI applications that transform client capabilities.

LSEG★3.7 (402)· London

You'll work with Java 17+, Spring Boot, Docker, Kubernetes, Kafka, GitLab, PostgreSQL, and AWS services (Aurora, MSK, S3) to build event-driven microservices with blockchain integration capabilities. LSEG, a global financial infrastructure provider, is launching a new cloud-based post-trade market infrastructure service, and you'll contribute across the full engineering lifecycle while collaborating with cross-functional teams in an agile environment.