Design, build, and deploy GenAI and ML solutions using OpenAI APIs, RAG, Python, Databricks, Spark, Delta Lake, and AWS (S3, Redshift, Lambda). You will work on predictive models and enterprise GenAI apps for Product, Sales, Finance, Marketing, Customer Success, and Engineering, owning projects end-to-end and integrating with Salesforce and Oracle platforms.

Design and build production-grade ML systems using MCP servers, recommendation systems, RAG pipelines, LLMs, and foundation models with Python, TensorFlow, PyTorch, FastAPI, and LangChain. You will own the end-to-end AI lifecycle for a sales data platform, deploying models into batch/real-time environments to drive forecasting, segmentation, and early warning systems. This role focuses on embedding context-aware intelligence into decision-making tools for business stakeholders.

FDM Group★2.9 (270)· London

You'll work with Python, pandas, JSON, REST APIs, LLMs, RAG, vector embeddings, and semantic search to build data ingestion pipelines and scalable platforms that enable AI-powered analytics and decision support within a leading global investment bank.

You will lead personalization, ranking, recommendation, search-relevance systems, experimentation (A/B testing, causal inference), and generative AI (LLMs, RAG, agentic experiences) at Tripadvisor Experiences. You own the ML strategy and technology for the marketplace that helps travelers discover and book tours and activities. You will lead and grow a unified ML organization through a layer of managers and senior scientists.

Rail Safety and Standards Board★3.8 (17)· London

Senior Data Engineer roles at RSSB using SQL, Python, Airflow, Databricks, Power BI/Fabric, Git, and FastAPI to build data pipelines, APIs, and data stores for analytics and data-driven tools from rail-industry safety, performance, and operational datasets.

Design and build production-grade ML systems including MCP servers, recommendation engines, and RAG pipelines. Work with Python, LLMs, LangChain, FastAPI, PyTorch, Spark, SQL, vector databases, and AWS/GCP on a sales data platform to create chatbots, semantic search, and forecasting models. Own the full AI/ML lifecycle—from problem definition to deployment and monitoring—to drive sales strategy and decision-making.

You will work with Python/C++, Ray, Spark, Flink, Kafka/Beam, and ML frameworks for search/recsys and frontier AI (LLMs, RL, multi-agent orchestration). Your role involves building enterprise superintelligence systems—end-to-end architectures that take ownership of business outcomes—as a founding scientist on a small, senior team.

You'll lead Python and FastAPI development on Lendable's Data Team, building data pipelines, APIs, and services deployed via Docker, Kubernetes, and AWS with ArgoCD. The fintech company rebuilds consumer finance products (loans, credit cards, car finance) to serve UK and US markets faster than traditional banks. You'll mentor a small team of 1–2 engineers while owning technical direction and remaining hands-on with code.

You will work with Databricks, Neo4j, Python, FastAPI, Spark/PySpark, SQL, and cloud-native platforms. The role focuses on building graph-driven data platforms and backend data services for advanced analytics and knowledge graph use cases at Acuity Analytics, a data and digital transformation solutions provider.

FDM Group★2.9 (270)· London

Python, pandas, JSON, REST APIs, FastAPI, LLMs, RAG, vector embeddings, semantic search, Plotly, and Plotly Dash. You will build scalable data platforms and AI-powered solutions for a leading global investment bank, enabling intelligent scenario modelling and decision support.

You will design and deploy GenAI and ML solutions using Python, Databricks, Spark, Delta Lake, and AWS (S3, Redshift, Lambda). Work includes building LLM workflows with OpenAI APIs, RAG, and prompt engineering, plus traditional models and enterprise integrations with Salesforce and Oracle. This Data Science & AI team builds real-world AI and automation that drives decisions across Product, Sales, Finance, and more.

Build production-grade LLM and agentic workflows using Python, and design evaluation, tracing, and quality-control systems for AI outputs. The role works on counterfactual analysis, forecasting, and decision support for business decision-making. As a founding engineer, you will work directly with the founders on product direction and technical architecture.

Elsevier★3.8 (769)· London

Work with Python, retrieval-augmented generation (RAG) pipelines, LangGraph, embeddings, re-ranking models, and platforms like Databricks. You will design and optimize lexical, vector, and hybrid search systems at scale, and define evaluation strategies for IR and GenAI across Elsevier's products.

Elsevier★3.8 (769)· London

You will work with Python, PyTorch, Hugging Face, LangChain, LangGraph, Haystack, and Databricks. You will build and evaluate LLM-powered workflows, RAG pipelines, and retrieval systems for LeapSpace, Elsevier’s AI-powered research assistant.

Quilter★3.4 (120)· London

Work with Python, PyTorch, LangChain, Hugging Face, Databricks, and Azure to develop and deploy traditional and GenAI machine learning models, including LLM and RAG pipelines. The role is in the AI Centre of Excellence at Quilter, a wealth management firm overseeing £126.3 billion in customer investments.

You will use Python, SQL, PySpark, and ML libraries to build predictive models for churn, propensity, store recommendations, and lifetime value. The CLV team analyzes large-scale player behaviour data to drive personalized engagement and commercial decisions across the PlayStation player lifecycle.

Monzo★3.5 (108)· London

Use Python, scikit-learn, SQL, and Go to build and deploy ML models including user embeddings, contextual bandits, and personalized ranking algorithms. You'll work on the Monzo Co-pilot search team, helping customers effortlessly find answers and products for their financial lives.

Work with transformer architectures (BERT, GPT, T5, Time-Series Transformers), PyTorch, PySpark, SQL, and cloud-based data tooling. On the AI Strategy, Research & Enablement team, you will design and prototype next-generation models for retail challenges, driving generative AI, multimodal learning, and representation learning. Collaborations include engineering, product, and academic partners.

Elsevier★3.8 (769)· London

You will work with Python, PyTorch, Hugging Face, LangChain, LangGraph, Haystack, Databricks, and visualization tools. This role focuses on designing, building, and evaluating advanced AI for LeapSpace, Elsevier's AI-powered research assistant, and the broader Search & AI Platform to accelerate scientific discovery.

Elsevier★3.8 (769)· London

You’ll work with LLMs, generative AI, RAG, NLP, Python, PyTorch, Hugging Face, LangChain, LangGraph, Haystack, and Databricks. The Platform Data Science team develops foundational AI for LeapSpace, Elsevier’s AI-powered research assistant, and the Search & AI Platform, helping researchers discover knowledge faster.

You'll develop Python-based analytics tools using FastAPI, Flask or Django alongside React, JavaScript/TypeScript, and Node.js, with Databricks and modern data lake architecture. The role supports a London Market insurer's exposure analytics team, building tools for underwriting decisions, portfolio optimization, catastrophe risk management, and AI-enabled solutions including natural language querying and agentic workflows. You'll collaborate across Data, Infrastructure, Claims, Pricing and Underwriting teams on exposure calculation engines using Open Exposure Data formats.

You'll work with Google Cloud Platform technologies including BigQuery, Gemini, Python, and SQL to design and deploy scalable data and AI solutions, particularly conversational analytics and LLM-based agentic systems. At Valtech, an experience innovation company serving major brands, you'll blend data engineering with AI/ML innovation, building production-ready solutions that translate business challenges into analytics and AI products while engaging directly with clients across technical and strategic decisions.

Lead Python Engineer at Lendable builds data pipelines, APIs and services using Python, FastAPI, Docker, Kubernetes and AWS to power the company's US fintech products in loans, credit cards and car finance. You'll balance hands-on coding with mentoring a small team of 1–2 engineers while shaping the data platform architecture as Lendable scales into the US market.