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.

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.

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.

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 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.

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.

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.

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.

BlackRock★3.8 (632)· London

You will design and develop distributed Java applications using Spring, Spring Boot, REST APIs, streaming technologies like Kafka and Apache Flink, relational databases (MS SQL Server, PostgreSQL, Sybase ASE), and Microsoft Azure. You will join BlackRock Global Markets’ In-Business technology team, building reliable, scalable technology to support the firm’s investment processes and platform architecture.

You'll work with Python (pandas, OOP), SQL (PostgreSQL, Snowflake), CI/CD pipelines, Git, and GitHub. You'll develop and support in-house fund management tools that assist Fund Managers in managing funds for Legal & General's asset management division.

You will deploy ML models and engineering surrogates (e.g., deep learning on CAE/CFD/FEA data) to production environments, using Python, PyTorch, Pandas, fastAPI, Scipy, and Kubeflow. PhysicsX builds an AI-driven simulation software stack for engineering and manufacturing.

Atlassian★3.9 (61)· London

Leading a distributed EMEA team, you'll use Python, JavaScript, APIs, and microservices to build AI-powered solutions with generative AI, LLMs, and agent-based frameworks. The Forward Deployed Engineering team partners with top organizations to solve complex problems using Atlassian’s AI capabilities.

You will work with GenAI tools like OpenAI, LangChain, Hugging Face, Vertex AI, and Bedrock, plus Python, PyTorch, TensorFlow, vector databases (FAISS, Pinecone), and cloud platforms. The Data Science Team builds and deploys LLM-based applications for enterprise use cases such as customer service, document automation, summarization, and knowledge retrieval.

You'll design, build, and maintain scalable AI data pipelines, cloud-native backend systems, and distributed services using Python, REST APIs, and microservices with FastAPI, Flask, or Django. You'll productionise Machine Learning, NLP, and Generative AI models, work with SQL databases, AWS/Azure, Docker, and Kubernetes, and mentor juniors. The company specialises in AI and data engineering, transforming AI research into production systems.

Build and enhance data-driven solutions using Scala, Python, Java, Hadoop, and Spark (Hive nice to have) in a financial services environment. Work with cross-functional teams on scalable, high-performance applications, participating in code reviews and performance optimization.

Use Python (pandas, NumPy, SciPy, matplotlib/seaborn) and SQL with cloud-based data tools and large language models. Build predictive and statistical models to identify alpha signals from alternative data for a leading investment manager with a global presence.

You will execute A/B tests, multivariate tests, and personalisation campaigns using platforms like Optimizely, VWO, and Adobe Target, working with analytics suites such as Google Analytics 4 and Adobe Analytics, plus CMS and CDP tools. The role drives daily execution of the Rapid Iteration & Optimisation framework, collaborating with Paid Media, CX, and Development teams to deliver performance-enhancing site updates.

Works with A/B testing tools (Optimizely, VWO, Adobe Target), analytics (GA4, Adobe Analytics) and CDPs (Segment, Tealium), plus basic HTML, CSS, JS. Executes daily high-velocity site optimisation and continuous improvement for global clients within WPP Enterprise Solutions, collaborating with Paid Media, CX, and Development teams.

You will work with Python, FastAPI/Flask, PostgreSQL, MySQL, BigQuery, GCP and/or AWS, and IAC/CI/CD tools to build production-grade data pipelines and MLOps infrastructure. You’ll join Ki’s commercial performance insights squad to upgrade algorithm decision data to a versioned event stream and support a Monte Carlo simulation engine for an algorithmic insurance carrier. The role is in an agile, cross-functional squad.

RBC★3.9 (5.5k)· London

You will drive the BI team's transition from Power BI to custom Databricks Apps, using React/Vue/Angular, REST, GraphQL, MCP, and DevOps tools (Git, CI/CD, GitHub Actions/Azure DevOps). You will build user-facing data visualisations and interactive analytics. You will mentor engineers and set technical standards.

RBC★3.9 (5.5k)· London

You will design, build, and maintain REST and GraphQL APIs and MCP servers using Python (FastAPI, Flask) serving ML models from Databricks for internal apps, CRM platforms, AI agents, and LLM applications. You will build middleware, prototype applications, and mentor junior colleagues in a team reporting to the Director, Data Science.

Arup★4.0 (352)· London

Python, Git/GitHub, pytest/unittest, CI/CD pipelines, AWS/GCP/Azure, Terraform/Docker, MATSim, Java, and PopulationSim. The Transport Modelling and City Modelling Lab teams build agent-based and activity-based models to solve urban transport challenges, using data and software engineering to plan future mobility systems.

You will engineer agentic AI decision loops, retrieval and embedding pipelines, and rigorous testing frameworks using Python, orchestration frameworks, vector databases, and semantic search, with JVM languages as a plus. Own the full model lifecycle, balancing fine-tuning specialized models versus frontier LLMs, while mentoring on production-grade ML standards. This is a senior ML engineering role for iForce Connect, building deep, integrated AI systems that navigate complex datasets.