Lovable★4.0 (43)· London

Uses Go, Rust, TypeScript; AWS, Azure, Google Cloud, Cloudflare; Kubernetes, Terraform, Temporal, OTEL, CI/CD; PostgreSQL, ClickHouse, Firestore, Spanner, BigQuery. Builds the platform for Lovable Enterprise enabling secure, cloud-agnostic deployment, defining architecture for sandboxing, networking, encryption, identity, and mentoring engineers.

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.

Native Kotlin and Jetpack Compose development on a consumer-facing Android app for in-store grocery trips, running on a known device inside live stores. Integrates on-device edge AI (ML Kit, TensorFlow Lite) and uses AI coding tools like Cursor or Claude Code. Reports to the Head of Android.

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.

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.

A Senior AI Engineer at Stakemate will use agent frameworks (LangGraph, LlamaIndex, Semantic Kernel, ADK), RAG, Python, cloud services (Azure/GCP/AWS), and SQL to move AI from experiments to production. They will shape the platform and architecture for a live betting product at a fast-growing social sports betting startup.

Microsoft★4.2 (9k)· London

Work with AI and cloud platforms such as Azure ML and OpenAI, applying practices including prompt engineering, retrieval-augmented approaches, model tuning, and CI/CD tooling. As a member of the Frontier Company Engineering team, you will embed within customer teams to build production-ready systems and services that solve complex organizational challenges.

You will work with agent frameworks (LangGraph, LlamaIndex, Semantic Kernel, ADK), RAG pipelines, vector stores, Python, SQL, and a major cloud (Azure AI Foundry, GCP, or AWS). You will take AI agents from proof-of-concept to production within Stakemate’s live sports betting product, setting the governance and standards for the company’s AI estate. Reporting to the CTO, you will shape the platform and architecture as the company scales.

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.

You will build AI-powered products and platforms using Golang, Kubernetes, and cloud-native microservices. This role is on Citi’s AI Product & Platform team within the Chief Technology Office, creating scalable backend services and APIs for enterprise AI applications.

This postdoctoral role works with GitHub, GitLab, CI/CD, SBOMs, vulnerability databases, cloud services, AI, and ML evaluation ontologies. The TrustAttest project develops a continuous compliance monitoring and automated remediation platform for SME software vendors, mapping the UK SSCoP requirements to observable technical signals across development environments.

You will work with Python, PyTorch, TensorFlow, and causal inference tools (DoWhy, EconML, CausalML) to build production AI pipelines, APIs, and microservices. You will design and deploy behavioural, sequential, and causal AI models into Digital Twins, agentic decision engines, and intelligent automation. No team size is specified.

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.

Work with Azure, Terraform, Azure DevOps, container platforms, and AI agents to build and automate cloud infrastructure. You will design deployment pipelines, observability, and engineering tooling that enable rapid delivery of both AI-powered and traditional cloud-native applications.

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.

Cognition builds Devin and Windsurf, AI software agents that enable engineering teams to automate complex tasks. As a Partner Deployed Engineer covering Europe, you'll work with Global Systems Integrators to deploy and scale these platforms across their client base, leading technical enablement, supporting pilot deployments, and building repeatable solutions. You'll need strong software engineering fundamentals, experience with enterprise or services delivery models, and comfort traveling 25–50% including to India.

You'll develop full-stack software products using TypeScript, React, Python, Node.js, Java/Spring, C#/.NET, SQL, NoSQL, vector databases, LLM frameworks, and cloud platforms (Azure, AWS, GCP) with Docker and CI/CD. QuantumBlack, AI by McKinsey is a multidisciplinary group creating industry-shaping digital products and tools for clients across industries. You'll work hands-on with global technologists, designers, and strategists to build cutting-edge solutions while mentoring client teams on engineering practices and Agile values.

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.

The researcher will work with GitHub, GitLab, CI/CD pipelines, SBOMs, vulnerability databases, cloud services, ontologies, and machine-learning evaluation for AI-assisted compliance monitoring. The TrustAttest project, an Innovate UK initiative, builds a continuous compliance monitoring and automated remediation platform that maps the UK’s Software Security Code of Practice to observable technical signals for SME software vendors.

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.

Morgan Stanley★3.8 (5.4k)· London

Work with AI coding tools (e.g., GitHub Copilot, Codex, Cursor, Claude Code) and modern engineering workflows (PRs, CI/CD) to drive adoption across the Firm. This role sits in the Firmwide developer tools group, MSDE, which shapes the SDLC for 25,000+ developers.