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.

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.

Google★4.3 (6.2k)· London

You will work with C++, Java, Kotlin, TypeScript, front-end frameworks, API development, machine learning, agentic coding, and GCP. This role is in Google Labs Science, which partners with DeepMind to transform GenAI research (e.g., Co-Scientist, AlphaEvolve) into products that accelerate scientific discovery.

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.

Google★4.3 (6.2k)· London

Work with C++, Java, Kotlin, TypeScript, front-end frameworks, APIs, and GCP to design and build machine learning and agentic systems. As part of Google Labs Science, you will partner with Google DeepMind to transform GenAI research into products that accelerate scientific discovery.

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.

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.

Design and deploy enterprise-grade AI applications using Python, LLMs (OpenAI, Claude, Gemini), RAG, vector databases, and LangChain/LangGraph on Azure/AWS with Docker and Kubernetes. Ascendion is an AI-first software engineering services provider delivering transformative solutions across multiple regions.

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.

Work with BigQuery, Airflow, BigTable, DynamoDB, Python, Scala, Spark, TensorFlow/PyTorch, Docker, Kubernetes, and LLMs building end-to-end ML pipelines. You'll own ML-powered features for the Recommendations Experience team, connecting 200M+ users with personalized music.

The Head of Technology will work hands on with Python and SQL (C#/Java beneficial) to build out the hedge fund's internal platform, covering trading and portfolio data flows, analytics and risk applications, research tooling, and operational systems. This role leads the technology function from the ground up, with scope to build a team as the fund scales.

Lead 6-7 engineers on core ML infrastructure for a systematic quant fund: distributed model training, LLM hosting/fine-tuning, and scalable deployment. Use Python with working knowledge of C++/Java. Own technical direction for real-world, high-stakes systems.

You will work with Python, JavaScript/TypeScript, AWS/GCP/Azure, LLM APIs (OpenAI/Anthropic), RAG, embeddings, vector databases, and SQL to build and deploy production-grade AI systems for equity research, automating screening, idea generation, and building internal APIs and UIs for an investment team.

Design and develop AI projects using Python, Java, TypeScript, Go, REST APIs, and databases. The team builds and deploys the full lifecycle of generative AI applications for customers, including RAG, agents, and fine-tuning. No team size is stated.

EXL Service★3.7 (2k)· London

You will work with Python, FastAPI, LLM frameworks (prompt engineering, RAG, agentic systems), and cloud AI platforms (Azure OpenAI/AWS Bedrock/Claude) on Kubernetes. As a Sr. AI Architect & Client Partner at EXL, a data and AI services company, you will design and deliver production-grade Generative and Agentic AI systems for enterprise clients, converting ambiguous mandates into secure, scalable solutions while shaping deals and reusable IP.

Google★4.3 (6.2k)· London

Manage engineers building Google’s Learning Platforms within the Global Business Organization, which powers Google’s ecosystem of products and services for customers and partners. Work with back-end languages (Java, Python, Golang, C++) and front-end (JavaScript, TypeScript, HTML, CSS), plus ML model fine-tuning and evaluation.

This role develops generative AI applications using Java, Python, React or Angular, LangChain, LlamaIndex, Hugging Face, Docker, Kubernetes, and AWS, with tools like GitHub Copilot. You will build scalable, AI-powered solutions for Citi’s global finance operations.

Work with Nvidia GB200 72NVL CX8 and AMD MIxxx architectures, coding in C, C++, C#, Java, JavaScript, or Python to benchmark, profile, and debug GPU clusters. You will help build foundational models powering Microsoft’s personalized AI assistant, Copilot, spanning training and inference infrastructure, model architecture, and data curation.

White & Case★4.0 (120)· London

Works with Python, JavaScript, MCP, agentic AI frameworks, LLMs, and cloud platforms (Azure, AWS) to architect and deploy AI tools. As technical lead of the Applied AI Lab, the role oversees design, development, testing, and deployment of AI solutions to support legal practice and firm-wide innovation. Manages and develops a multidisciplinary team of legal engineers.

You will work with Python, JavaScript, Model Context Protocol (MCP), agentic AI frameworks, LLMs, and cloud platforms (Azure, AWS). As technical

bp★3.8 (5.2k)· London

You will work with Python, Java, Azure, LLMs, vector DBs, and RAG patterns, using tools like LangChain, Haystack, and Hugging Face. As part of bp's AI organization, you will design and deploy scalable, production-grade AI applications such as conversational bots and AI-powered search for the oil and energy sector.

The role involves Python, FastAPI, Docker, Kubernetes, CI/CD, MLOps tools (MLflow, Weights & Biases), and workflow orchestration (Airflow, Prefect, Kubeflow) for deploying and monitoring ML models in production. You'll build and maintain ML infrastructure, pipelines, and observability for an edge computing environment at a company scaling its ML platform.

You will work with Python, Java, Azure, LLMs, vector DBs, RAG, and frameworks like LangChain, Haystack, Hugging Face, and Crew AI. The AI organization builds and deploys scalable, production-grade AI-powered applications for business value within the oil and energy/HR technology sector.

bp★3.8 (5.2k)· London

Design, develop and maintain production-grade AI applications using Python, Java, microservices, AWS/Azure/GCP, CI/CD, LLMs, vector DBs, RAG, and tools like LangChain, Haystack, Hugging Face, Weaviate, and LlamaIndex. The team builds scalable AI-powered applications—including conversational bots, AI search, unstructured data processing, and generative BI—to solve high-priority business problems.