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 will work with Python, AI coding agents (e.g., Claude Code, Codex), and potentially AWS, Kubernetes, and Temporal. Unitary builds Virtual Agents — AI-powered software workers that automate complex workflows end-to-end in customer tools, focusing on commercial insurance, marketplaces, and primary healthcare.

Use deep learning frameworks, distributed computing, GPU-based training, and MLOps to build and deploy batch and real-time ML models. The Search & Recommendations team develops personalised recommendations, product ranking, and AI styling experiences for ASOS's online fashion platform, helping customers discover products.

You'll work with PyTorch, TensorFlow, XGBoost, GPUs, CI/CD and containerisation on the Search & Recommendations team, building production-grade ML systems for personalised recommendations, product ranking and AI-driven styling at ASOS, covering the full lifecycle from experimentation to deployment.

Senior Machine Learning Engineer at ASOS, building production ML systems for personalized outfit recommendations and style discovery. You'll work with deep learning frameworks like PyTorch/TensorFlow, distributed GPU infrastructure, and MLOps practices including CI/CD, containerization, and observability. Collaborate with ML Scientists on batch and real-time models serving millions of customers.

FDJ UNITED★4.0 (96)· London

You'll lead a DataOps/MLOps team building an ML platform and data operations capability for FDJ United's Sportsbook, working with AWS, Terraform/CloudFormation, Spark, Kafka, and CI/CD pipelines for end-to-end ML lifecycle management. The role involves establishing best practices, monitoring, and governance while collaborating with Data Engineering, ML Engineering, Quants, and Data Science teams to personalize customer experiences and manage risk.

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.

AXA★3.9 (2.9k)· London

Design and implement secure architectures for enterprise security and AI/ML initiatives across AXA, using Docker, Kubernetes, cloud security, API security, micro-services, MLOps/MLSecOps platforms, threat modeling, and SSDLC. Defines technical security standards and reference architectures aligning with frameworks like NIST AI RMF and ISO/IEC 42001. Reports to the Chief Security Officer.

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.

Manage a team of test and DevOps engineers building CI/CD, Yocto-based embedded Linux, test automation, and cloud infrastructure (Python, C/C++, AWS/GCP/Azure) for non-invasive neuromonitoring medical devices.

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.

JPMorgan★3.9 (20k)· London

You will work with Python, AWS, EKS, Docker, Kubernetes, Kafka, and MLOps tools. As a member of the Applied AI/ML Software Engineering team within CIB Security Services Technology, you will build cloud-based frameworks for hosting machine learning models alongside Data Scientists to integrate AIML solutions into production systems.

Lead engineers use Java, C#/.NET, Python, TypeScript, JavaScript, React, Go, Ruby, Scala, R, and SQL; Git, MacOS/Linux, and CI/CD; and AI-assistive tools. They lead multi-client software consultancy engagements, building and delivering production agentic systems and ML-powered features. The team is typically one to two clients per year.

Deloitte★3.9 (14k)· London

You will work with large language models, prompt engineering, retrieval-augmented generation, orchestration frameworks, and cloud platforms (Azure, AWS, GCP). As a GenAI Solution Design lead in Deloitte's AI Scaling & Transformation team, you design enterprise-grade GenAI solutions for Security & Justice clients in regulated environments. This Senior Manager role involves overseeing delivery across multiple workstreams and leading mentoring delivery teams.

Lead a team of test and DevOps engineers owning CI/CD, Yocto-based embedded Linux, and test automation infrastructure. The role uses Python, C/C++, infrastructure-as-code (Terraform, CDK), and cloud platforms (AWS/GCP/Azure) for a non-invasive neuromonitoring medical device company.

Sovos★3.3 (105)· London

Design and build Snowflake architecture and dbt model infrastructure for the topline financial data chain (billing, ARR, revenue recognition) at Sovos, a global tax compliance leader. Use advanced SQL, dbt, and optionally Python, Azure/AWS, and MCP to deliver production pipelines that support FP&A, Accounting, and AI consumers through financial close.

Quantiphi★3.3 (60)· London

As an AI Engineer at Quantiphi, you will work with the GCP stack (AlloyDB, GCS, GKE, Gemini Enterprise, Agent Platform, BigQuery, Cloud Run, Vertex AI Pipelines), advanced Python, and tools for agentic AI, MLOps (Kubeflow, Docker, Kubernetes), and CI/CD. The company is an Applied AI and Big Data services firm delivering industry solutions across Healthcare, Financial Services, and more, with a focus on Generative AI and Agentic AI.

Prima★3.6 (45)· London

You'll work with Kafka, Flink, Spark, Databricks, Python, AWS, Redshift, PostgreSQL, and DevOps/CI-CD tools to build data products and pipelines for Prima's pricing and underwriting domain. The role bridges machine learning and engineering, focusing on data architecture, transformation, governance, and enabling teams to ingest, store, and serve data products. You'll join over 350 engineers across Prima, an online motor insurance provider trusted by 5 million drivers expanding in the UK and Spain.

Work with Python, Pandas, NumPy, Scikit-learn, SQL, LLMs, RAG, and MLOps to design, build, and deploy enterprise-scale AI/ML solutions improving operational efficiency, service resilience, and customer outcomes at LSEG, a global financial markets infrastructure and data business.

Design and build the Snowflake schema and dbt model architecture for the financial data pipeline, owning ingestion, transformation, and close-ready outputs for FP&A and Accounting. You will also extend dbt models for OPEX, COGS, and commissions, and integrate CRM/ERP systems like NetSuite or Salesforce. This role supports Sovos’ global tax compliance platform.

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.

Working with Kubernetes, Terraform, Docker, and AWS, Azure or GCP, you will design and implement a unified infrastructure platform enabling reliable model deployment. The team supports highly available, scalable production environments, with desirable experience in Kubeflow and ML tooling.

Manage multiple teams across Platform Engineering, SRE, DevOps, MLOps, Security and Infrastructure Engineering, using Kubernetes, Azure, Terraform, GitHub Actions, ArgoCD, and observability tooling. The teams build and operate the core platform, cloud infrastructure and automation that underpin the company's data, analytics and AI ecosystem. The scope covers hiring, workforce planning and organisational scaling across infrastructure disciplines.

XPS Group★3.3 (18)· London

Design, deploy and manage machine learning models in production using Azure ML, Python, SQL, and CI/CD. You will work within XPS Analytics, a multi-disciplinary team of actuaries and data scientists, to extract insights for pension scheme clients.