Work with Python, Snowflake, Power BI, Git, CI/CD, and cloud deployment to productionize data science and AI prototypes. You will bridge raw data ingestion to analytics-ready assets, building and maintaining analytics workflows and dashboards for a global investment firm.

Lead a team of Data Scientists and ML Engineers building AI solutions for financial markets using LLMs, Generative AI, Deep Learning, Transformers, Agentic AI, Python, PyTorch, TensorFlow, Hugging Face, LangChain, Semantic Kernel, Azure AI Foundry, AWS AI Services, and MLOps/LLMOps practices.

Infosys★3.8 (17k)· London

You will architect CI/CD pipelines using the Harness platform (CI, CD, STO, Chaos Engineering, CCM, AI Agents), Kubernetes, Terraform, Helm, OPA, and cloud services. As a Harness SME, you drive DevOps transformations, migrating enterprise teams from legacy tool chains and implementing DevSecOps governance.

Work with Python, LLMs, RAG pipelines, vector databases, Hugging Face Transformers, LangChain, PyTorch, and Docker to build production AI systems. Design, develop, and deploy AI-powered solutions such as conversational AI, document intelligence, and backend services for the Global Analytics team.

Lead a team of DataOps and MLOps engineers building and evolving the ML platform and data operations for FDJ United’s Sportsbook product. You’ll work with AWS, Terraform, CloudFormation, Spark, Kafka, and CI/CD pipelines to enable end-to-end machine learning workflows from research to production.

You will build scalable server-side logic using JavaScript, TypeScript, and Node.js in a microservices architecture on AWS with Kubernetes, Terraform, Kafka, and Snowflake. You will join the MMH team within the Martech domain, which orchestrates campaign delivery across email, push notifications, and personalised content for all Sainsbury’s Group brands. The team includes 7 engineers.

You'll use Python, SQL, shell scripting, containerization, infrastructure-as-code tools, and Scala/Java or Python to build and maintain scalable data pipelines. You'll support a financial technology company's data infrastructure, contributing to AI/ML experiences and international applications. No team size is stated.

Design, build, and operate a Kubernetes-based MLOps platform using Helm, Terraform, Kubeflow, KServe, vLLM, and JupyterHub. The platform supports the full model lifecycle—experimentation, deployment, and scalable inference—for AI, ML, and LLM workloads in production.

You'll build and operate a Kubernetes-based AI/ML platform using Helm, Terraform, Kubeflow, KServe, vLLM, and JupyterHub. This role focuses on creating infrastructure, tooling, and guardrails so MLOps and data science teams can reliably deploy and serve models at scale.

You will work with AWS, Python, SQL, ETL/ELT pipelines, data warehousing, and APIs. You will own the data platform for a company developing AI and machine learning products that help enterprise clients make decisions. You will start as a senior individual contributor with the opportunity to build and lead a future data team.

You’ll work with SQL, Python, Snowflake, AWS/GCP, and APIs to build and evolve the firm’s cloud-based data platform, developing scalable data pipelines and integrating data to support investment decisions. You will mentor two junior Data Engineers.

Trainees will learn Linux, Git, Docker, AWS, Azure, Google Cloud, CI/CD, and Infrastructure as Code. The program provides structured training and practical DevOps projects to build a portfolio. No prior experience is required.

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.

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.

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.

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.

You will own the dbt project (models, tests, CI/CD) and work with BigQuery (or Snowflake/Redshift), advanced SQL, and a BI tool like Looker or Metabase. As an Analytics Engineer for a £100m+ D2C e-commerce brand, you'll transform raw data into trusted dashboards that drive marketing, finance, and operational decisions.

Salesforce★4.2 (1.5k)· London

Success Architect works with Agentforce, Data Cloud, Salesforce Clouds, and data platforms like Snowflake, Databricks, AWS. They drive customer success by optimizing technical architecture for data unification and AI-driven automation, enabling personalized engagement and real-time decision-making.

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.

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.

Virtusa★3.7 (1.1k)· London

Work with Snowflake, DBT, Python, SQL, and AWS services to build enterprise data platforms. The role supports a global pharma engagement, designing scalable data solutions for advanced analytics, clinical insights, and business intelligence.

Snowflake, DBT, Python, SQL, and AWS. Leads architecture of enterprise data platforms for a global pharma engagement supporting clinical insights, business intelligence, and data governance.

Work with SQL, Python, and modern data technologies (dbt, Spark, containers, DevOps tooling, orchestration tools, Git, AI/ML). As a Solution Engineer for the UK market, you present Snowflake’s technology and vision, working hands-on with prospects to demonstrate value through demos, proof-of-concepts, and design.