Citi★3.9 (21k)· London

You will work with C++, C#, .NET, Java, Python, kdb, and SQL to develop analytics libraries and pricing models using Monte Carlo methods and PDE solvers. The role focuses on creating quantitative models for Citi’s cross-asset XVA and capital analytics, supporting trading business pricing and risk-management. You will collaborate with Traders, Structurers, and technology professionals.

Wise★3.5 (42)· London

You will work with Java, Spring Boot, AWS, Apache Kafka, and potentially Trino. The Pricing Core team builds and maintains the main pricing platform that enables Wise to price its products, processing over 100 million requests daily across critical paths.

JPMorgan★3.9 (20k)· London

Work with KDB, Q, AWS, and optionally Terraform, Kubernetes, Python, C, C++, or Java. Develop and maintain applications storing large Options data sets for real-time and historical access within JPMorgan’s Equities Trading Data & Analytics team, migrating to AWS and re-architecting systems.

Allianz★3.8 (3.4k)· London

You'll build production data/ML systems using Python, Docker, Kubernetes, Terraform, Azure, Azure DevOps or GitHub Actions, plus SQL, PySpark, dbt, and Microsoft Fabric. Joining a small agile team, you'll deliver real-time API endpoints serving 10M+ daily requests and evolve cloud infrastructure and

You will work with Python, PyTorch or JAX, Docker, CI/CD, and cloud platforms to deploy ML models for protein design. The team builds scalable infrastructure for training, inference, and evaluation across molecular and protein datasets, taking research prototypes into production.

Allianz★3.8 (3.4k)· London

Use Docker, Kubernetes, Terraform, Azure, Azure DevOps, and Python to build and maintain production-ready data and ML systems, including real‑time API endpoints serving 10M+ daily requests. You will join a small stream‑aligned agile team that develops cloud‑based infrastructure and automated deployment pipelines.

Atlassian★3.9 (61)· London

Leads a team of forward-deployed engineers building AI-powered solutions with Python, JavaScript, APIs, microservices, and LLMs. The team partners directly with top organizations to apply Atlassian’s AI capabilities to solve real-world problems.

EXL Service★3.7 (2k)· London

You will work with Generative AI tools (OpenAI, LangChain, Hugging Face, Vertex AI, Bedrock), LLMs, vector databases (FAISS, Pinecone, ChromaDB), and Python frameworks (Transformers, PyTorch, scikit-learn). As part of EXL’s Data Science Team, you’ll build and deploy GenAI solutions for enterprise use cases such as customer service, document automation, summarization, and knowledge retrieval.

Stanley David and Associates★2.3 (3)· London

Work with Snowflake, GitHub, Octopus Deploy, SQL, Python, PowerShell, Shell scripting, and cloud platforms (Azure, AWS, or GCP). Lead design, implementation, and deployment of cloud-based data platforms, including CI/CD pipelines and ELT/ETL processes. Mentor developers and provide technical leadership across project teams.

Junior Software Engineer role supporting in-house fund management tools for Legal & General's Index Analytics team, using Python (pandas, OOP), SQL (PostgreSQL, Snowflake), git/GitHub, and supporting CI/CD pipelines. The work covers portfolio performance analysis, process automation, and data pipelines for fund managers. Exposure to JavaScript, Flask/FastAPI is helpful.

You will work with AWS, Python, Kafka, Kubernetes, gRPC, Terraform, CloudFormation, Docker, and CI/CD tools like Jenkins or GitHub Actions. AeroVect builds autonomous ground-handling systems for airlines, and you will own the cloud infrastructure and data pipelines that power their real-world deployments.

Millennium Management★3.8 (21)· London

You will work with Python, Linux, SLURM, Kafka, Solace, Pulsar, Memcache, Redis, Datadog, ELK stack, SQL, NoSQL, Kdb, Docker, and Kubernetes. The EQRT Services team develops high-performance real-time analytics and portfolio management platforms for intraday trading, risk assessment, and backtesting.

Design, develop, and optimize scalable data solutions using Snowflake, GitHub, and Octopus Deploy, with CI/CD pipelines and cloud platforms (Azure, AWS, GCP). This role leads the design, implementation, and deployment of modern cloud-based data platforms, including ELT/ETL pipelines and secure automated deployments.

You will work with Generative AI, LLMs, OpenAI, LangChain, Hugging Face, PyTorch, TensorFlow, FastAPI, Streamlit, and cloud platforms (AWS/GCP/Azure). You will design and deploy GenAI solutions—including RAG pipelines, fine-tuning, and prompt engineering—for enterprise use cases such as customer service, document automation, and knowledge retrieval on the Data Science Team at a global data and AI company.

Work with AWS, Python, Kafka, Kubernetes, gRPC, Docker, Terraform, CloudFormation, and CI/CD tools (Jenkins, GitHub Actions, CircleCI). You will design, build, and scale the data pipelines and cloud infrastructure powering AeroVect's autonomous driving platform, owning systems from the ground up.

You’ll build and maintain scalable data pipelines and implement observability, monitoring, and data quality checks using Python, SQL, shell scripting, containerization, and IaC tools (with Scala/Java preferred). You’ll support AI/ML-driven financial technology platforms at a global fintech company, collaborating with product engineering, data science, and business teams.

Python, SQL, Apache Spark, Airflow, Azure Data Factory, Docker, and Kubernetes. You will design, build, and maintain data-driven applications, integrating with data lakes, lakehouse architectures, and enterprise databases for large-scale analytics systems.

Deloitte★3.9 (14k)· London

Work with Python, CI/CD, PyTorch, MLFlow, FastAPI, Azure (Fabric, Data Factory, ML Studio, Synapse, Cosmos), and LLM tools (RAG, embeddings). You will lead end-to-end AI/ML projects for Deloitte’s Tax & Legal Data and AI Studio, delivering analytics and AI-driven client solutions.

FDJ UNITED★4.0 (96)· London

Builds scalable ETL/ELT pipelines and modernizes a multi-cloud data platform using Python, Spark, Airflow, dbt, SQL, and AWS services (S3, Athena, Redshift). Works with Lakehouse architectures (Iceberg, Delta Lake) on an agile team.

American Express★4.1 (9.7k)· London

AI Researcher at American Express will develop advanced ML models for structured data (tabular, sequences, graphs) using transformer, reinforcement learning, and causal techniques, with Python, Spark, C/C++, Java, and Google Cloud tools like Vertex AI and BigQuery. The role sits in AI Labs within Credit and Fraud Risk, driving research into production for credit, fraud, and marketing decisions. Leadership scope is not specified.

Build and deploy AI agents that interpret geopolitical and financial signals, using Python, SQL, Docker, Kubernetes, AWS (RDS, EKS, S3), GitHub Actions, BM25, vector search, hybrid retrieval, reranking, and knowledge graphs. You’ll own the full lifecycle of agentic systems, from prototyping to production, partnering with Research, Product, and Engineering.

You'll work with Python, SQL, ETL/ELT, FastAPI, and AI/LLMs to build a mission‑critical Data Migration Platform that connects to legacy CRMs and onboard financial agencies at scale.

Write Python and FastAPI to build a real-time intelligence platform for private markets, used by venture capital and private equity firms to analyze live data and make faster investment decisions.

You will work with C#, .NET, ASP.NET, Angular, SQL Server, Oracle, Docker, Grafana, Kafka, Azure Service Bus, and Azure DevOps. The role is on the Data team of a global energy trading house, building data consumption and publishing platforms that extract value from large-scale market data. You will lead end-to-end delivery, mentor developers, and shape the platform's architecture.

Work with LLMs, prompt engineering, fine-tuning, RAG, and vector databases (FAISS, Pinecone, ChromaDB) using Python, LangChain, Transformers, PyTorch, TensorFlow, and cloud platforms. As part of the Data Science team, you will design, develop, and deploy enterprise-grade GenAI solutions for customer service, document automation, summarization, and knowledge retrieval.