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

Build and maintain batch and streaming data pipelines on AWS using dbt, Spark, SQL, Kafka/Flink, and Airflow. Work on the Sportsbook Data Platform, a modern event-driven lakehouse transforming sportsbook data (bets, offers, etc.) into curated datasets for analytics, trading insight, and machine learning.

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.

Work with Microsoft Fabric, Databricks, Azure, Snowflake, Python, Spark, SQL/NoSQL, PowerShell, Azure Data Factory and Azure DevOps to build a Fabric Data Lakehouse for USS’s Investment Management business, implementing scalable ETL/ELT frameworks across a medallion architecture. Share knowledge across a small team of data engineers and collaborate with architects, business stakeholders and data teams.

Work with Microsoft Azure data platforms: Azure Synapse, Azure SQL Server, Azure Data Factory, Azure DevOps, Microsoft Fabric, and OneLake; also uses DBT and Spark. You will modernize core data platforms—ingesting data from MS Dynamics CRM, Finance & Operations, and SAP SuccessFactors—to power analytics, AI-driven insights, and executive decision-making.

SmartestEnergy★4.1 (15)· London

You will work with Microsoft Azure Data Platform, Microsoft Fabric, PySpark, SQL, Python, Azure DevOps, CI/CD, and Power BI to build enterprise-scale data pipelines and Data Lakehouse/Data Warehouse solutions. The team develops modern cloud-based data solutions supporting ETL/ELT processes across Medallion Architecture for SmartestEnergy’s energy digitisation and decarbonisation goals.

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.

You will lead Azure Databricks architecture, designing enterprise Lakehouse solutions with Apache Spark, PySpark, Delta Lake, Azure Data Factory, Data Lake Gen2, Event Hubs, Synapse, Python, SQL, Terraform, and CI/CD. This is part of a major Energy & Utilities transformation programme, specifically supporting critical Energy Trading data.

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.

You'll work with Trino, Iceberg, dbt, Airflow, Kafka, Python, SQL, Java or Scala to build and optimize data pipelines on a Lakehouse following Medallion Architecture (Bronze, Silver, Gold layers). You'll join Wise’s Data Onboarding Team, part of the Finance Squad that stewards the company’s financial truth by processing hundreds of millions of high-throughput financial events daily into audit-ready structures. The team abstracts product complexity into reusable, domain-agnostic stream-enrichment pipelines.

McKesson★3.5 (7.7k)· London

Senior Data Engineer: Snowflake, dbt, Airflow, ADF, Python, Databricks, Azure, AKS, Infrastructure as Code, CI/CD. You will own and build the ClarusONE data platform, designing scalable pipelines and establishing engineering standards for reliability, security, and compliance.