Hudson River Trading★4.5 (2)· London

You will work with Python, Pandas, NumPy, R, and MATLAB to apply statistical analysis and machine learning to vast quantities of market data. The team builds and maintains predictive trading models for the company's high-performance trading infrastructure. You will research, develop, and test order execution methods to increase trading efficiency.

Hudson River Trading★4.5 (2)· London

You will write and maintain critical trading infrastructure using C/C++ or Python on Linux, working closely with engineers who design trading algorithms in a fast-paced, real-time environment. The role is with Hudson River Trading, a firm that brings a scientific approach to trading financial products.

Hudson River Trading★4.5 (2)· London

Software engineering interns work with Python, C, or C++ on independent programming projects and gain hands-on exposure to HRT's algorithmic trading research and infrastructure.

Hudson River Trading★4.5 (2)· London

You will use Python, C++, Pandas, NumPy, R, or MATLAB to build and maintain predictive trading models. As an algorithm developer, you research and test novel order execution and model training methods, applying

DeepL★3.8 (4)· London

You'll work with C# / .NET, RESTful APIs, distributed architectures, and optionally React/TypeScript, plus MCP, CLIs, OAuth, and AI coding tools. You join the API Enterprise team, which builds enterprise adoption features (usage insights, cost controls, RBAC, compliance, data residency) for DeepL's Language AI API platform.

Salesforce★4.2 (1.5k)· London

Success Architect works with Agentforce, Data Cloud, Salesforce Clouds, and data platforms like Snowflake, Databricks, AWS. They drive customer success by optimizing technical architecture for data unification and AI-driven automation, enabling personalized engagement and real-time decision-making.

The role involves developing and maintaining in-house C++ pricing libraries, multi-threaded servers, and automated trading systems, while advancing the quantitative toolbox with new algorithms. You will work on model frameworks, signal research, execution and portfolio construction tools, using Python and C++ with QuantLib experience preferred.

C++ developers will work on ultra-low-latency exchange protocol encoders and automated trading strategies, using C++ and optionally Python, for a data-driven prop fund’s global automated trading systems. The role involves collaborating with researchers and ops in a small, fast-paced team operating in a real-time environment.

You'll develop systematic HFT and market-making strategies in cryptocurrency markets using Python for research, signal generation, backtesting, and live performance analysis. Working with your own firm's capital, you'll own the full research lifecycle—from hypothesis to production deployment—optimising execution quality, latency, inventory risk and spread capture across global exchanges.

EXL Service★3.7 (2k)· London

Design AI architectures on Azure using .NET, React, LLMs, RAG, OCR, and agentic frameworks (LangGraph, AutoGen, CrewAI) to build production-grade GenAI platforms. The role focuses on processing complex insurance documents for underwriting, claims, and reinsurance use cases. You will lead and mentor a team of .NET, React, and GenAI developers.

You'll work with frontend (TypeScript, React, Angular, Vue, Next.js, Tailwind), backend (Python, Node.js, Java/Spring, C#/.NET), databases (SQL, NoSQL, vector databases), LLM frameworks, and cloud platforms (Azure, AWS, GCP) including Docker and CI/CD. QuantumBlack, an AI division of McKinsey, builds industry-shaping products and tools across diverse industries by pairing software engineers, AI engineers, and data scientists with client leadership. As a Senior Software Engineer, you'll lead architecture design, mentor teams in engineering excellence and Agile practices, and drive technology decisions on scalable solutions.

Design AI architectures on Azure using .NET, React, LLMs, Azure OpenAI, RAG, Agentic AI (LangGraph, AutoGen, CrewAI), OCR, microservices, and Azure AI Search. Build scalable GenAI platforms for intelligent automation in insurance functions like underwriting, claims, and reinsurance. Lead and mentor .NET, React, and GenAI developers.

DeepL★3.8 (4)· London

You will work with React, C#/.NET, and AI-powered tools like GitHub Copilot and Claude, building full-stack Enterprise features. The team, part of DeepL’s Business Acceleration Track, architects the company’s revenue and billing systems, including Identity & Access Management and subscription architectures for global enterprises.

DeepL★3.8 (4)· London

Work with C#/.NET Core, Python, Kubernetes, and React on DeepL’s Language AI platform—building translation, writing, and real-time voice solutions used by over 300 million users globally. You will own revenue-critical systems end-to-end, from architecture to production.

DeepL★3.8 (4)· London

You will work with C#/.NET Core, Python, React, and Kubernetes, plus AI-assisted tools like Copilot and Claude. You’ll ship end‑to‑end user‑facing features for DeepL’s Language AI platform, which provides human‑like translation, improved writing, and real‑time voice translation to millions of users and businesses globally.

Lead engineers use Java, C#/.NET, Python, TypeScript, JavaScript, React, Go, Ruby, Scala, R, and SQL; Git, MacOS/Linux, and CI/CD; and AI-assistive tools. They lead multi-client software consultancy engagements, building and delivering production agentic systems and ML-powered features. The team is typically one to two clients per year.

Build and ship AI features using LLMs, retrieval-augmented generation (RAG), and Python or .NET, deploying on Azure. You will join Shawbrook’s AI Engineering squad to move AI from proof-of-concept into production-grade systems supporting lending, deposits, operations, risk, and customer experience.

Design, build, and deploy LLM/RAG pipelines, agentic workflows, and backend APIs in Python on Azure. You will drive production-grade AI for lending, deposits, risk, and customer experience at Shawbrook Bank. Mentor other engineers in the AI Engineering squad.

Prima★3.6 (45)· London

You'll work with Kafka, Flink, Spark, Databricks, Python, AWS, Redshift, PostgreSQL, and DevOps/CI-CD tools to build data products and pipelines for Prima's pricing and underwriting domain. The role bridges machine learning and engineering, focusing on data architecture, transformation, governance, and enabling teams to ingest, store, and serve data products. You'll join over 350 engineers across Prima, an online motor insurance provider trusted by 5 million drivers expanding in the UK and Spain.

Design, build, and deploy quantitative models using Python, R, and libraries (numpy, pandas, scikit, pytorch, tidyverse, caret, ggplot) plus NLP tools, SQL, AWS, Sagemaker, DataBricks, Snowflake, MLFlow, and Weights & Biases. The team creates systematic country risk analytics and ML/NLP pipelines for BMI’s market insights, supporting clients in risk assessment.

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.

Work with Python, Azure, CI/CD, and LLM/RAG pipelines, including vector search and .NET. Build production-grade AI systems for lending, deposits, operations, risk, and customer experience in a regulated bank. Member of the AI Engineering squad.

You will work with Python or .NET, LLMs, retrieval-augmented generation (RAG), Azure, CI/CD, and APIs. The AI Engineering squad builds production-grade AI systems for lending, deposits, operations, risk, and customer experience at a bank.

Work with Python (pandas, NumPy) and tree-based machine learning to analyze high-frequency limit orderbook data and build predictive signals for proprietary trading. DV Trading provides liquidity to worldwide markets and hedging opportunities. You’ll collaborate with senior researchers and traders to refine production-ready models.

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.