Work with Python, SQL, JavaScript/TypeScript, NumPy, Pandas/Polars, visualization libraries, and deep learning frameworks (PyTorch, JAX, or TensorFlow) to design and deploy full-stack data-driven applications and machine learning solutions. You will be embedded with Arsenal's Men’s First Team Analysis department, building tools that directly support coaching and analysis workflows.

Escalent★3.6 (38)· London

You’ll work with Python and frameworks like Django, Flask, or FastAPI, building RESTful APIs, microservices, and data pipelines while integrating AI/ML models and LLM APIs. Tech includes SQL/NoSQL databases, AWS/Azure, Docker, Kubernetes, and CI/CD. Escalent is a data analytics and advisory firm, and you’ll develop AI-powered applications and intelligent workflows in its cloud-native environment.

You will build evaluation, experimentation, and observability pipelines using Python and platforms like Braintrust, LangSmith, Arize, and Weights & Biases, plus design LLM-as-judge and human-in-the-loop scoring. The role focuses on AI-native consulting for clients like PayPal and adidas, including production monitoring, quality signal detection, and cost attribution. You'll also wire evaluation gates into CI/CD pipelines for the AI Engineering team.

This Senior Full Stack Developer role involves Python, TypeScript, React, SQL, PostgreSQL, Large Language Models (LLMs), prompt engineering, and AI coding tools. You will own and develop the company’s flagship AI platform, building data pipelines and full-stack features that solve strategic challenges for clients. You will work within a small engineering team.

You will work with kdb+, q, Python or Java, APIs (REST, messaging), Linux, AWS/GCP, Docker/Podman, and CI/CD. You will develop, test and maintain kdb+ applications and supporting services for market data and analytics on a Client Engineering team for a FinTech leader. You will also mentor junior developers.

You'll work with Python, LLMs, Generative AI, RAG architectures, Agentic AI, Conversational AI, Voice AI, Speech-to-Text and Text-to-Speech technologies. This role designs, builds and deploys AI-powered customer and business solutions across Conversational AI, Agentic AI, Voice AI and intelligent automation for a leading international consumer technology and digital services organisation.

You will develop and deploy LLM-powered applications using PyTorch, SGLang, and PEFT/LoRA, building and fine-tuning embedding and reranking models. You will work on an early-stage platform that combines machine learning with real-world trading workflows to transform specialised financial markets. You will join a small engineering team with direct collaboration alongside founders and traders.

Develop agentic AI applications with Python, LLMs (OpenAI, Anthropic), and frameworks like LangGraph or CrewAI, building REST APIs and integrating cloud services (AWS, Azure, GCP). This contract role involves AI solutions for a client in financial services, working with structured financial data and collaborating with product and engineering teams.

You'll work with Python, FastAPI/Django/Flask, PostgreSQL, cloud platforms (Azure/AWS/GCP), and build scalable REST APIs and data pipelines for an enterprise AI platform used by financial institutions.

Capgemini★3.7 (11k)· London

Work with LLMs, vector databases, prompt engineering, multi-agent frameworks, and AWS (SageMaker, ECS, Fargate, Docker, Kubernetes) to design reusable architecture patterns and integrate GenAI into enterprise applications, APIs, and data ecosystems. The role builds AI-powered solutions for Capgemini, an AI-powered global business and technology transformation partner.

Build REST and GraphQL APIs and MCP servers in Python (FastAPI, Flask) to expose ML models from Databricks to internal apps, CRM platforms, and AI agents at RBC. Work within the Data Science team to architect the serving layer, handling contract design, authentication, and documentation.

Senior Machine Learning Engineer building production AI systems that help UK consumers find financial products. You'll use Python, MLflow, Kubernetes, Databricks, Airflow, LangChain/LangGraph, Terraform, and CI/CD pipelines to design and deploy batch and real-time ML models, LLM-powered capabilities, and monitoring tooling. You'll work closely with data scientists, engineers, and product teams to productionise experimental models and evolve the internal ML platform.

You will work with Python (pandas, NumPy, SciPy, matplotlib/seaborn), SQL, cloud data tools (ideally Azure), and large language models (LLMs) to build predictive models and identify alpha signals. You will join the data science team of a leading investment management company.

Wise★3.5 (42)· London

You will build scalable systems for business onboarding and verification using Java or JVM languages, Spring framework, JDBC, and relational/non-relational databases with CI/CD pipelines and distributed systems. This engineering team owns end-to-end projects to automate verification, create flexible onboarding journeys, and manage compliance risks for Wise’s money-moving platform across Europe and the UK.

You'll work with Google Ads Scripts, APIs, SQL, AI/LLMs, value-based bidding, and automation tools to own VOY's multi-million-pound UK paid search channel and scale it internationally. The role involves building bidding systems, search strategies, and measurement with Data, Product, and CRO teams.

LexisNexis Legal & Professional★3.6 (1k)· London

You will drive AI ideation and integrate LLMs, NLP, and machine learning into Practical Guidance editorial workflows. This global role partners with commercial, product, and engineering teams to deploy scalable content-optimization solutions across North America, UK, and Asia-Pacific.

Work with Python, PyTorch, JAX, CUDA, Docker, Kubernetes, Slurm, Ray, and Linux on GPU computing. The Molecular Intelligence team builds ML and physics-based infrastructure for drug discovery, including biophysics, chemistry, cofolding models, simulation workflows, and agent-usable scientific pipelines.

Work with OpenMM, OpenFE, molecular dynamics, MM/GBSA, and free-energy perturbation (RBFE/ABFE) to build validated biophysics workflows for Lila Sciences’ agentic drug discovery platform. Define scientific requirements and validation standards so AI agents can reliably use biophysics tools. Collaborate with computational scientists, ML researchers, and research engineers.

Relay Technologies★2.0 (9)· London

At Relay Technologies you will build and own time-series forecasting models for parcel volume, inbound international volume, and parcel size/weight, using Python and ML frameworks. You will work on the Network squad’s demand-forecasting engine that predicts parcel movement across sortation, middle-mile, and last-mile operations, directly affecting cost per parcel. This is a hands-on individual-contributor role, serving as the technical anchor for Demand Forecasting, collaborating with a Senior Data Scientist, ML Engineer, and Analyst.

Work with Python, SQL, graph databases, graph ML, Docker, Kubernetes, and LLMs. Own and evolve Kharon's risk propagation engine that analyzes entities in a knowledge graph to surface risk signals for global security clients.

Chubb Insurance★3.7 (1.3k)· London

You will use Python, Hugging Face Transformers, LangChain, LlamaIndex, PyTorch, TensorFlow, Docker, and cloud platforms to build and deploy LLM, RAG, and conversational AI applications. As part of Chubb Insurance's Global Analytics team, you'll design production-ready AI solutions for business value.

You'll work with Java/JVM and Spring on the backend, React/JavaScript/TypeScript on the frontend, plus CI/CD, distributed systems, and relational/non-relational databases. You'll join Wise's Account Integrity team, building fraud-prevention and financial-crime detection systems to protect customer accounts. Wise is a global technology company for moving and managing money.

Tesco★3.5 (25k)· London

You will work with Java, Spring Boot, Kafka, Apache Flink, Apache Beam, NoSQL databases, Redis, and Apache Camel. The Tesco Personalisation team builds real-time, low-latency services for recommendations, offers, and experimentation across web, mobile, and in-store channels using streaming data and ML.

This senior software engineer role uses Python, Snowflake, dbt, Kafka, Java, AWS, and Linux/Docker. You will build ELT pipelines and a streaming platform for tick data, consolidating legacy systems as part of the data engineering team for a systematic hedge fund.

Use Python, SQL, graph databases, Docker, Kubernetes, and LLMs to own Kharon’s risk-propagation engine that evaluates entities in a knowledge graph and surfaces risk signals. The role spans research, data engineering, and product to evolve a production system for global security analytics.