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.

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.

Senior Data Engineer role owning the ClarusONE data platform, building pipelines with Snowflake, dbt, Airflow, ADF, Python, and managing IaC on Azure/AKS. You will establish engineering standards, ensure data lineage and governance (SOX), and collaborate with Analytics, Data Science, and Software Engineering teams.

Work with LangChain, RAG, FastAPI, React, DuckDB, GCP, Python, and production LLMs. Build agentic AI systems automating digital forensics and incident response investigations for an AI-native security company. This is a 0-to-1 founding role shipping across the full stack.

Work with the Databricks Data Intelligence Platform, Spark, PySpark, Python, Java or Scala, and major public cloud platforms (AWS, GCP, Azure). As a pre-sales engineer, you will demonstrate how Databricks' unified data and AI platform helps customers solve complex data challenges, collaborating with Account Executives and Senior Solutions Architects.

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.

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

You will work with Python, FastAPI, Node.js, Docker, Kubernetes, and AWS to build shared backend services, frameworks, and developer tooling. This role designs, builds, and maintains the platform foundations that internal engineering teams use to build, integrate, and deploy software.

You'll work primarily with React and TypeScript on the frontend, and Python (FastAPI/Flask) on the backend, using tools like Storybook. The Monolith AI Engineering Team builds the core platform for engineering simulation and AI workflows, aiming to become a super-intelligent AI test lab for the engineering industry.

GSK★4.1 (5.4k)· London

You'll design, build and operate scalable cloud infrastructure and model-serving systems supporting AI/ML workloads at GSK using Python, FastAPI, GCP (Cloud Run, GKE, Cloud SQL), and major deep learning frameworks. The AI for Science team develops production-grade software enabling drug discovery and personalized medicine through state-of-the-art AI and machine learning applied to biomedical data.

You’ll work with Python/TypeScript, FastAPI/Flask/Express/NestJS, AWS/Azure/GCP, Kubernetes, Docker, Terraform, PostgreSQL, and DynamoDB. You’ll build backend infrastructure for Scale’s Generative AI Platform, delivering APIs and distributed systems that bring GenAI into production for large enterprises.

You'll work with Azure, Terraform, Kubernetes, Docker, CI/CD tools (Jenkins, GitLab CI, GitHub Actions), Python, and monitoring platforms like Prometheus and Grafana to build and scale infrastructure for Orbital Copilot, an AI assistant that accelerates commercial real estate due diligence for law firms. As the second SRE, you'll design cloud infrastructure, implement container orchestration, develop automated pipelines, and establish reliability practices from the ground up in a startup environment.