Monzo★3.5 (108)· London

You will work with LLMs, autonomous agents, RAG, sequence-based architectures, Python, SQL, and Go, developing and deploying machine learning models in production. You’ll lead the design of a human-in-the-loop system that predicts, identifies, and routes customer problems to scale support for Monzo’s banking products across multiple geographies.

You'll design and own end-to-end RAG pipelines for payments domain content using Python, Azure Data Factory, Functions, ADLS, CosmosDB, and AI Search, with CI/CD via GitHub Actions and Azure DevOps. RedCompass Labs builds AI-powered payment transformation and financial crime detection solutions, including the RedFlag Accelerator for identifying human trafficking. You'll collaborate with AI engineers and payments experts to turn regulatory documents and handbooks into a reliable retrieval layer for their proprietary multi-agent AI platform.

Senior Data Engineer at Softwire, a UK digital consultancy, working with Python, SQL, Databricks, Snowflake, Fabric, and AWS/Azure/GCP across data architecture, pipeline design, and governance. You'll lead complex data engineering projects for clients across public and private sectors—from healthcare research platforms to energy market infrastructure—while mentoring junior engineers and shaping technical direction on multi-stakeholder engagements.

Wipro★3.8 (17k)· London

You will work with GenAI, LLMs, RAG, agentic frameworks, cloud platforms, DevOps, MLOps, and data governance. As an AI Strategic Advisor, you will define enterprise AI strategy, lead integration of generative and agentic AI, and architect full lifecycle AI initiatives for a global technology consulting firm.

A Senior AI Engineer at Stakemate will use agent frameworks (LangGraph, LlamaIndex, Semantic Kernel, ADK), RAG, Python, cloud services (Azure/GCP/AWS), and SQL to move AI from experiments to production. They will shape the platform and architecture for a live betting product at a fast-growing social sports betting startup.

You will work with agent frameworks (LangGraph, LlamaIndex, Semantic Kernel, ADK), RAG pipelines, vector stores, Python, SQL, and a major cloud (Azure AI Foundry, GCP, or AWS). You will take AI agents from proof-of-concept to production within Stakemate’s live sports betting product, setting the governance and standards for the company’s AI estate. Reporting to the CTO, you will shape the platform and architecture as the company scales.

Python, PyTorch, JAX, LLMs, vLLM, vector databases and modern agent frameworks will be used. The stealth AI startup is building a pre-launch, next-generation AI assistant for everyday tasks, conversations and workflows — a 0-to-1 opportunity on a small engineering team.

You will architect Citi's Release on Demand platform, defining architecture, scalability, reliability, observability (tracing, logging, metrics), and E2E testing. The platform codifies governance and automates SDLC deployments for internal engineers. You'll lead a small, agile team distributed across Belfast, Canada, and London, mentoring engineers and owning the technical roadmap.

Work with Java, GitHub Copilot, Devin.AI, Citi Squad, Stylus Workspaces (Gemini, OpenAI, Claude, Llama), Docker, Kubernetes, and CI/CD. Build high-performance, real-time FX risk management systems for a greenfield platform with a 10+ year strategic commitment. As a VP, lead end-to-end delivery and technical direction.

You will work with Python, LLMs, agentic workflows, retrieval-augmented generation (RAG), and internal AI platforms. The Tactical AI Automations team deploys and supports AI tools across PIMCO's Portfolio Management and Analytics groups, including operational support for Lane Assist and POET (portfolio optimization tools).

Design, build and deploy production Generative AI applications, LLM-powered solutions and agentic frameworks using Python, LangChain, LangGraph, and OpenShift. You will automate enterprise workflows with intelligent agents, working with PostgreSQL and pgvector as part of Citi’s COO-Technology team building scalable AI for global operations. This is an Individual Contributor Staff Engineer role.

You will build AI-powered products and platforms using Golang, Kubernetes, and cloud-native microservices. This role is on Citi’s AI Product & Platform team within the Chief Technology Office, creating scalable backend services and APIs for enterprise AI applications.

S&P Global★3.9 (1.7k)· London

Lead the AI+ strategy for S&P Global's Finance team, using AI/ML, NLP, and GenAI to improve finance processes and operational efficiency. This role will develop and execute the roadmap for automation and robotics, partnering with Corporate Finance and divisional CFOs. The team is within the Enterprise Finance and Strategy group.

You will use Salesforce and conduct multi-threaded outreach via phone, email, and social channels. Decagon builds a conversational AI platform whose AI agents power personalized customer interactions across voice, chat, email, and SMS.

EPAM Systems★3.8 (703)· London

You will work with AI, GenAI, cloud platforms (AWS, Azure, Google Cloud), mobile technologies, digital commerce, and omnichannel architectures. You will lead technology consulting engagements for retail and CPG clients, shaping digital strategies, defining enterprise and solution architectures, and driving AI-driven transformation roadmaps. The first engagement requires mobile expertise.

Bloomberg★3.8 (1.1k)· London

Work with Bloomberg Terminal data, Python, visualization tools (Power BI, Tableau, Qlik Sense), generative AI, machine learning, automation, and advanced Excel. Support the Technology equity research team by building financial models and conducting investment research on AI companies including Alphabet and Meta.

Google★4.3 (6.2k)· London

Google Cloud AI offerings (Vertex AI, Gemini), modern multi-agent systems (LangGraph, CrewAI). This role drives Gemini Enterprise adoption by industrializing AI excellence, moving licenses to activated, and ensuring production-grade deployment of AI agents and workflows. It leads the GE Accelerator efforts.

Data Idols★0.0· London

You will use Python, machine learning, recommender systems, LLMs, Generative AI, conversational

HSBC★4.0 (16k)· London

Work with Python, Docker, Kubernetes, Flask/FastAPI, PyTorch/TensorFlow, SQL Server/Postgres, and CI/CD pipelines to build and deploy production-grade ML and GenAI services. You will join the Growth AI programme within HSBC’s Corporate & Institutional Banking Data & Analytics, developing AI applications to identify business opportunities and mitigate client attrition.

Moody's★3.6 (589)· London

You will work with Python, TypeScript, Java, or Go; cloud platforms (AWS, GCP, Azure); Docker, Kubernetes, PostgreSQL, MongoDB, Redis, vector databases; and build AI applications using LLMs, RAG, prompt engineering, and AI agents. The Innovation team builds next-generation internal and external products powered by large language models, AI agents, machine learning, and natural language processing.

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.

Edited★2.7 (3)· London

You will work with Python, *nix, LLMs, MCP, Django, Flask, FastAPI, Elasticsearch, and SQL databases. You will build an AI chatbot with agents and tooling, and scale a large-scale retail data infrastructure on AWS and bare metal for the company's AI-driven retail intelligence platform.

Pipedrive★4.2 (5)· London

Lead a growing team of Applied Scientists and AI/ML Engineers at Pipedrive, a cloud-based CRM platform serving over 100,000 companies. You'll own the technical strategy for AI/ML solutions working with structured CRM data and unstructured content, designing autonomous agentic workflows using LangGraph, RAG architectures, vector databases, and LLMOps infrastructure. The role requires 8+ years of applied ML/AI experience in production environments, deep expertise in modern AI engineering, cloud infrastructure (AWS/GCP), and distributed systems, with proven ability to lead and scale technical teams.

You'll build scalable data infrastructure for AI applications using Python, SQL, Spark, dbt, LangChain, and cloud platforms (AWS, Azure, GCP), working with data pipelines, embeddings, vector search, and LLMOps tools like Terraform and LangSmith. QuantumBlack, McKinsey's AI division, develops next-generation AI systems and enterprise solutions for clients across industries. You'll collaborate with Data Scientists and ML Engineers in cross-functional Agile teams based in London, supporting the firm's AI adoption and complex problem-solving at scale.

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.