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.

Work with Python, PyTorch (or JAX/TensorFlow), RL fine-tuning frameworks like TRL or verl, and distributed multi-GPU training. The Reinforcement Learning Team advances RL, Bayesian optimisation, AI agents, LLMs, and vision-language models, applying them to AI for science, chemistry, physics, and robotics while publishing at top venues.

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

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.

Citi★3.9 (21k)· London

Build and scale AI-powered financial systems using Java, Spring, React/Angular, Python, FastAPI, Docker, and GitHub Copilot within Citi’s global tech environment. You will design generative AI agents, LLM workflows, and RAG pipelines while working on enterprise microservices, mobile, or front-end development. This role is for an individual contributor on an agile team.

You'll work with Python, FastAPI, PydanticAI, Langgraph, Langchain, React, AWS or GCP, and containerization/infrastructure-as-code to build production AI systems. The role involves full-stack development—front and back-end—for a FinTech firm's internal AI Centre of Excellence, delivering GenAI solutions including chatbots, assistants, and workflow automation across the enterprise. The team is expanding and expects to triple headcount this year.

Productionise GenAI and agentic systems using Python, FastAPI, LangGraph, LangChain, or similar frameworks, along with Docker, GitHub, CI/CD, and AWS. You will design APIs and backend components that integrate LLMs with tools and data sources, bridging the gap between prototypes and production systems for the energy and commodity market intelligence provider Argus Media. As a senior member of the Data Science team, you will mentor colleagues and set engineering standards.

Fitch Ratings★3.8 (136)· London

You'll build ML/AI solutions using Python, FastAPI, PyTorch/TensorFlow, LangChain, LangGraph, and React/TypeScript within cloud-based microservices architectures. The agile team delivers software products for Fitch's BMI business, which provides data-driven market insights and risk analysis to customers globally. You'll design full-stack enterprise applications, implement GenAI features with LLM integration, and establish development best practices across the product suite.

Deloitte★3.9 (14k)· London

You will work with Python, JavaScript, Kafka, Azure, Kubernetes, Terraform, Helm, LangChain, PySpark, Databricks, and ADLS. You will design, develop, and deploy production-grade GenAI and machine learning systems for the GenAI team within Deloitte’s Data & AI organisation.

Staff AI Engineer will use Python, Azure, LangChain, Semantic Kernel, LLMs, and RAG to build production-ready patterns for agentic AI, prompt engineering, and responsible AI. This cross-cutting role connects AI ambition to delivery across all Tech domains at Sainsbury's, creating reusable accelerators and standardising AI engineering division-wide.

Research, design, and build production-grade ML models using Python, PyTorch, TensorFlow, or JAX, with deployment on cloud infrastructure (AWS, GCP, Azure) and on-device inference on clinical monitoring hardware. The work supports Circadia’s platform, which uses radar to capture contactless physiologic data paired with EHR-integrated clinical context, advancing novel areas like physiological foundation models and bed-exit detection. The role reports to a Principal ML Engineer.

Accenture★3.9 (29k)· London

You will work with Python, LangChain, LangGraph, AutoGen, FastAPI, REST APIs, vector databases, Git, CI/CD, and cloud platforms (Azure, GCP, or AWS) to build, deploy, and iterate on machine learning and generative AI solutions for clients across industries.

You will work with LLMs, generative AI, Python, JavaScript, React, HTML, CSS, Microsoft Azure, and DevOps tools (CI/CD, Git). You will design, build, and deploy AI applications that enhance productivity, automate workflows, and support business decision-making across a growing technology function.

Checkout.com★3.8 (15)· London

You will work with LLMs (GPT, BERT, Llama, Gemini), Python/JavaScript, AI frameworks (LangChain, PydanticAI), cloud platforms (AWS/GCP/Azure), Docker, Kubernetes, and Terraform. As part of Checkout.com’s AI Centre of Excellence, you will design, build, and deploy Generative AI solutions (chatbots, assistants, knowledge extraction tools) to enhance products and streamline business processes.

You'll work with Python (FastAPI), Go, TypeScript, PostgreSQL, Redis, Pinecone/pgvector, AWS (ECS, Lambda, SQS), Docker, Kubernetes, Terraform, LangChain/LlamaIndex, vLLM, and Anthropic/OpenAI APIs. You'll build and optimize RAG pipelines, real-time inference APIs, and vector search infrastructure powering LLM features for 500k+ monthly active users.

Flo Health★4.8 (26)· London

Lead a team building shared AI infrastructure powering Flo’s chatbot, cycle predictions, and health insights. Work with Databricks, OpenAI, Anthropic, and AWS, managing MLOps, model serving, and LLM stacks in production.

Trainline★3.9 (17)· London

You'll work with Python, Pandas, NumPy, Scikit-learn, Spark, Docker, Terraform, MLflow, CI/CD, and LLMs (fine-tuning, RAG, agents). The team builds production ML and AI solutions powering Trainline's search, recommendations, pricing, routing, personalization, digital marketing, and customer support across rail travel.

Works with Azure cloud, LLMs, vector search, MLOps, CI/CD, data pipelines, and machine learning techniques like NLP. Leads design and delivery of AI/ML solutions across multiple squads to support the bank’s AI strategy.

Ebury★3.3 (13)· London

You will work with React, TypeScript, Python, FastAPI, Strands Agents SDK, AWS Bedrock, RAG, Ragas, Langfuse, AWS/GCP, Terraform, and Docker. You will build Flow, an internal AI agent platform that orchestrates multi-step workflows across data warehouses and services to automate business operations. The role involves end-to-end ownership of agent workflows, reasoning UIs, and cross-system integrations.

Octopus Energy★3.5 (184)· London

Build and scale Generative AI systems using LLMs, RAG, and agents, working with async Python (asyncio, FastAPI), vector databases (Qdrant, Weaviate, Pinecone, pgvector), orchestration frameworks (LangGraph, LlamaIndex), and AWS. The role is on the Kraken platform team at Octopus Energy, building reusable AI services, knowledge retrieval pipelines, and governance layers to accelerate the clean-energy transition. No team size or scope is stated.

Work with Python/TypeScript, FastAPI/Flask/Express/NestJS, AWS/Azure/GCP, Kubernetes/Docker, Terraform, PostgreSQL/DynamoDB, and CI/CD tools. You will build core infrastructure for the Scale Generative AI Platform (SGP), designing scalable APIs, distributed data systems, and deployment pipelines for enterprise GenAI production