The intern will use Python (Pandas, NumPy), AWS (Kubernetes, Kafka), APIs, Snowflake, Streamlit, Git, Docker, Helm, and CI/CD. They contribute to front office software tools for real-time trading, market data analytics, and visualization, and work with the commercial trading team to optimize pricing models and trading workflows. The role is with Castleton Commodities International, a global energy commodities merchant and infrastructure investor.

Talan★3.2 (66)· London

You will design, build and maintain data pipelines using Python, sourcing and processing public and project-specific datasets. Supporting Talan's energy advisory team, you will deliver quantitative analysis on low-carbon technologies and policies to shape industry and investment decisions.

Senior Data Engineer role using SQL, Python, Java, Airflow, dbt, Spark, BigQuery, Snowflake, GCP, AWS, and DOMO. You will design and maintain scalable data pipelines for web analytics, CMS, ad tech, and social media, supporting analytics and reporting across Independent Media’s portfolio of digital news brands.

You will work with SQL, Python, AWS or Azure, Snowflake, Databricks, infrastructure as code, and CI/CD, plus AI tools like RAG, feature stores, and MCP. As a Data Architect on the AI Advisory team, you own data design across client engagements for a CFO advisory firm, defining architecture to support reliable AI, analytics, and reporting.

You will work with Python/R, SQL, pandas, numpy, Robyn, Meridian, PyMC, Bayesian and regression models, and MMM pipelines. You will join the Marketing Analytics team to measure and optimize marketing investments by owning the Marketing Mix Modelling pipeline and translating statistical insights into growth strategies.

Capgemini★3.7 (11k)· London

Work with graph databases (Neo4j, Amazon Neptune, TigerGraph, ArangoDB), Azure AI Search, Elasticsearch, Pinecone, Python, Azure, AWS, Kubernetes, Docker, and CI/CD. Design and implement an enterprise knowledge platform enabling AI agents across Insurance, Payments, and Healthcare domains. Lead platform strategy, enterprise architecture, and cross-functional engineering teams.

Python and/or C#/.NET, with experience using LLM APIs, retrieval-augmented generation (RAG), prompt engineering, and agent orchestration patterns. This director-level role defines and builds WTW’s enterprise-wide AI engineering capability, creating shared frameworks, reusable patterns, and reference implementations for production-grade generative AI and agentic solutions across the company.

Design, build, and deploy ML models using Python, TensorFlow, PyTorch, Scikit-learn, and LLMs with LangChain, RAG, and vector databases (Pinecone, FAISS). Deploy on AWS, Azure, or GCP, developing REST APIs with FastAPI/Flask. This role is with Aivra Health, a health technology company solving real-world business problems.

You'll work with Python, SQL, and production ML systems—feature pipelines, model registries, batch/real-time inference, and A/B tests—building recommendation, uplift, and churn models. The CRM tribe at Wise, a global money-movement company, uses these to recommend the right products to customers at the right moment across the customer lifecycle.

Quantiphi★3.3 (60)· London

Architect MLOps strategy and implement enterprise-grade ML/LLM pipelines using AWS SageMaker, EKS, Lambda, API Gateway, and CI/CD tools. Design hybrid MLOps/LLMOps workflows with governance, monitoring, and container-orchestrated platforms. Lead cross-functional delivery for an AI-first digital engineering company serving Fortune 1000 clients.

Work with Azure, AWS, Terraform, Kubernetes, Docker, Python, Go, and CI/CD pipelines to architect and operate secure, scalable cloud platforms for AI products at LSEG. This individual-contributor role owns the technical direction of cloud infrastructure, delivery pipelines, and operational capabilities, providing functional leadership across teams.

You'll use Java, Spring Boot, and AWS to build and maintain backend services, and Apache Kafka for event-driven architectures. The Pricing Core team at Wise builds and maintains the main pricing platform, processing over 100 million requests daily under strict SLAs.

Work with Python, SQL, C#, Palantir Foundry, Planlogic EOS ETRM, Spark/PySpark, AWS, and Stonebranch to design and maintain digital products for BP’s LNG Energy Trading and Risk Management landscape.

You will work with Python, SQL, PostgreSQL, Snowflake, git, GitHub, and CI/CD pipelines to support in-house fund management tools. The Index Analytics and Technology team within Legal & General’s Asset Management division builds and maintains these tools for portfolio performance analysis and data pipelines.

You will work with C++, Python, Linux, machine learning/statistical libraries, Google Cloud, Airflow, InfluxDB, and Grafana. The role builds data pipelines, prediction tools, and infrastructure for systematic equities trading, supporting quantitative research and large-scale computation.

The role involves using Python, C++, and QuantLib to develop and maintain in-house pricing libraries, multi-threaded servers, and automated trading systems. You will build tools for signal research, execution, and portfolio construction, working closely with traders and quantitative analysts on fixed income and FX derivatives.

You’ll develop components for a research and trading platform used by quant researchers and portfolio managers, working in Python and relational databases with optional AWS/GCP, CI/CD, and containerized environments. The team builds scalable backtesting tools, data pipelines, and production-grade research infrastructure. This is a Python developer role; no team size is stated.

You will develop predictive signals and systematic HFT and market-making strategies using order book dynamics, trade flow, and large datasets with Python. The proprietary firm trades its own capital in high-frequency cryptocurrency markets. You will own the complete research lifecycle, from hypothesis through live production and performance optimisation.

Quantitative researcher role using Python or C++ to develop signals, models, and trading strategies for global financial markets, plus research tools and data pipelines evaluated with advanced statistical techniques. Works with a small team of researchers and engineers on systematic trading research infrastructure. Candidates need a PhD or postdoc in a quantitative field.

Work with Python, PyTorch, TensorFlow, Scikit-learn, XGBoost, AWS (SageMaker, Lambda, S3), MLflow, LangChain and other MLOps/LLMOps frameworks to build and deploy ML and generative AI solutions. You will design production-ready models for forecasting, classification, and anomaly detection, as well as LLM-powered applications, supporting defence, government, and national security clients as part of a technology consultancy.

Python and C++ for building real-time market-data systems, cloud-based simulation platforms and production-deployment toolchains. This engineer designs infrastructure that lets quantitative researchers develop, test and execute predictive models at a systematic-investment firm currently spinning out from a hedge fund to become an independent manager.

Work with C# or Python, Azure (Fabric, Data Factory, ML Studio, Foundry, Synapse, Cosmos), GenAI, LLM APIs, FastAPI, Microsoft Agent Framework, LangChain, and vector databases. This role is on a 20-person AI team in a financial-sector company, building new projects and products that use data to drive smarter decisions.

Work with AI/ML systems, prompt engineering, multimodal models, retrieval systems, agent systems, computer vision, and backend services using Go, Python, or C. You will join a small R&D team building a cloud platform that transforms audio and video data into actionable intelligence for commercial clients.

You will work with SQL, Python, DBT, Fivetran, Airflow, Snowflake, Git, and cloud platforms (AWS, Azure, or GCP). The role designs, builds, and maintains scalable data platforms and pipelines that power analytics, reporting, and data-driven decision-making. No team size is stated.

Design and deploy generative AI applications on Azure and Databricks using LangGraph, FastAPI, Docker, and Kubernetes. The role builds production-grade LLM systems for underwriting, claims, and customer service at an insurer.