This role involves working with Python, PyTorch, JAX, CUDA, Linux, Docker, Kubernetes, Slurm, Ray, Flyte, and Argo. The team builds ML and physics-based infrastructure for drug discovery, including simulation workflows and agent-usable scientific pipelines. Engineers improve code quality, GPU efficiency, and distributed execution while making research tools scalable and maintainable.

Use Python, SQL, graph databases, Docker, Kubernetes, and LLMs to own Kharon’s risk-propagation engine that evaluates entities in a knowledge graph and surfaces risk signals. The role spans research, data engineering, and product to evolve a production system for global security analytics.

You will build AI agents using Python, SQL, Docker, Kubernetes, AWS (RDS, EKS, S3), GitHub Actions, RAG pipelines, BM25, vector search, hybrid retrieval, and knowledge graphs. These agents interpret geopolitical and financial signals for Kharon, a company providing global security risk intelligence.

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.

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.

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.

Use Python, Azure, Docker, Kubernetes, Apache Airflow, and GitHub Actions to build scalable ETL pipelines, financial data pipelines, and cloud-native infrastructure. This front-office role is at a systematic equity hedge fund modernising its quantitative research platform, working directly with portfolio managers and researchers. Experience with S&P Xpressfeed, Snowflake, Bloomberg, MSCI Barra, Databricks, and LLMs is advantageous.

Rebuild data infrastructure for a market-neutral systematic equities fund using Python, Microsoft Azure, Docker, Azure Kubernetes, Apache Airflow, and GitHub Actions for CI/CD. Work on ETL pipelines, vendor integration, and cloud-based research platforms, with relevant experience in Snowflake, Bloomberg, MSCI Barra, or Databricks. The role spans data engineering and quantitative development to transform financial data into investment signals.

Build and maintain scalable data pipelines (BigQuery, Cloud Composer, Airflow, Dataflow, Spark, Flink, dbt) on GCP for AI/ML experiences. Use Docker/k8s, Terraform, SQL, shell scripting, and monitoring tools (Monte Carlo, Looker, NewRelic, Splunk). Collaborate with product engineering, data science, and business teams.

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.

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.

You'll work with frontend (TypeScript, React, Angular, Vue, Next.js, Tailwind), backend (Python, Node.js, Java/Spring, C#/.NET), databases (SQL, NoSQL, vector databases), LLM frameworks, and cloud platforms (Azure, AWS, GCP) including Docker and CI/CD. QuantumBlack, an AI division of McKinsey, builds industry-shaping products and tools across diverse industries by pairing software engineers, AI engineers, and data scientists with client leadership. As a Senior Software Engineer, you'll lead architecture design, mentor teams in engineering excellence and Agile practices, and drive technology decisions on scalable solutions.

Build with Python, Go, or Java on the backend, and React/TypeScript on the frontend, using PostgreSQL and non-relational stores, with Docker, Kubernetes, and cloud infrastructure (AWS, GCP, Azure). This role owns full-stack delivery of the B2B product—a campaign, device management, and insights platform for retail and brand partners—along with API design and distributed systems. The team includes engineers, product, and platform groups, working closely with the Head of B2B Product.

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.

LiveScore Group★3.4 (14)· London

You will build and deploy robust data pipelines using GCP services (BigQuery, Dataflow, Pub/Sub, Cloud Storage, Bigtable, Spanner, Airflow, Terraform) and Python or SQL. As part of LiveScore Group's Data Team, you will develop scalable data systems for real-time AI/ML workloads that power sports media and betting for brands LiveScore, LiveScore Bet and Virgin Bet.

LiveScore Group★3.4 (14)· London

Use GCP (BigQuery, Dataflow, Pub/Sub, Cloud Storage, Bigtable, Spanner, Firestore), SQL, Python, Terraform, Airflow, Docker and AI tooling. You will lead the data team in designing scalable pipelines, data models and real-time systems that power sports media and betting brands.

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 will work with GCP services (BigQuery, Dataflow, Pub/Sub, Cloud Storage, Bigtable, Spanner, Firestore, Cloud Composer, Vertex AI), SQL, Python, Terraform, Airflow, and Docker. The data team builds scalable, real-time data pipelines and AI/ML workloads to power sports media and betting products for LiveScore, LiveScore Bet, and Virgin Bet. You will lead architectural decisions, own data-solution design, and mentor engineers.

You will work with GCP services (BigQuery, Dataflow, Pub/Sub, Cloud Storage, Bigtable, Spanner, Firestore), Cloud Composer, Airflow, Docker, Terraform, Python, and SQL. The team builds scalable data pipelines and models to power real-time AI/ML workloads for LiveScore’s sports media and betting brands.

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.

Work with Golang, Kubernetes, gRPC, RESTful APIs, HashiCorp Vault, and cloud-native tooling to build AI-powered platforms. The team, part of Citi's Chief Technology Office, defines the bank's tech and engineering strategy by engineering cutting-edge products at planetary scale.

You'll work with Python, C#, SQL, REST APIs, cloud platforms (Azure, AWS, Snowflake), CI/CD, Docker, and Git. You'll join a dedicated AI squad building and shipping production-grade AI-powered applications using Generative AI, LLMs, agentic AI, and prompt engineering for an investment management firm.

Build React + TypeScript features end-to-end for an AI-native data orchestration platform. The stack includes Python/FastAPI, TypeScript/Express, serverless AWS, Docker, PostgreSQL, and DynamoDB. You’ll run rapid UI experiments and ship multiple times a day in a small, elite team.

Expedia Group★3.8 (1.2k)· London

Backend role building pricing services for Expedia Group's travel platform using Java, Scala, Kotlin, Spring, MSSQL, DynamoDB, Redis. You'll design and maintain high-throughput, low-latency distributed microservices and caching mechanisms.