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.

Python, FastAPI, Docker, DuckDB (nice-to-have), JSON Schema, Git, and RESTful APIs. You will design and ship ETL pipelines that transform raw bank data into the FIRE regulatory reporting standard. Suade is a regtech SaaS company automating compliance and risk solutions for financial institutions.

Work with Python, FastAPI, Docker, DuckDB, JSON Schema and Git to design, build and ship client ETL pipelines that map raw bank data onto the FIRE regulatory reporting standard. You will maintain a shared ETL framework, write validated mapping code, and mentor junior engineers at Suade, a regtech SaaS company automating compliance for financial institutions.

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.

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 scalable distributed systems with Java (Spring/Spring Boot) and Python, using Kafka, MQ, XML, and CI/CD in Unix/Linux environments. This Risk Services team within CRISIL supports capital-markets risk data ingestion, processing, analytics, and reporting for Global Markets.

Working with Core Python, Pandas, NumPy, Flask/FastAPI/Django, SQL, and Unix/Linux, you will build backend applications and data processing pipelines for trading and risk management within an investment banking environment, supporting risk calculations, trade workflows, and reporting.

You'll build distributed backend services and microservices using Java (Spring/Spring Boot), Python, messaging frameworks like Kafka/MQ, and Unix/Linux, with CI/CD and containerization. The Role sits on the Risk Services team within Global Markets, designing scalable systems for risk data processing, analytics, and reporting workflows.

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.

Edited★2.7 (3)· London

You will work with Python, *nix, LLMs, MCP, Django, Flask, FastAPI, Elasticsearch, and SQL databases. You will build an AI chatbot with agents and tooling, and scale a large-scale retail data infrastructure on AWS and bare metal for the company's AI-driven retail intelligence platform.

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.

Capgemini★3.7 (11k)· London

Design, build, and manage data pipelines and models using AWS Snowflake, Azure Data Lake, Databricks, Redshift, and BigQuery. Automate CI/CD with Azure DevOps, GitHub Actions, Jenkins, GitLab CI/CD, and provision infrastructure with AWS CloudFormation, Docker, ECS, EKS, and Kubernetes. Capgemini is an AI-powered global business and technology transformation partner.

EXL Service★3.7 (2k)· London

You will work with Python, FastAPI, LLM frameworks (prompt engineering, RAG, agentic systems), and cloud AI platforms (Azure OpenAI/AWS Bedrock/Claude) on Kubernetes. As a Sr. AI Architect & Client Partner at EXL, a data and AI services company, you will design and deliver production-grade Generative and Agentic AI systems for enterprise clients, converting ambiguous mandates into secure, scalable solutions while shaping deals and reusable IP.

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 lead a small cross-functional data pod building data pipelines, models and services in Python, SQL and Snowflake to turn supply chain data into customer-facing sustainability insights. The role combines hands-on data engineering with technical leadership and line management of the pod's engineers.

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.