Citi★3.9 (21k)· London

You will work with TypeScript, React, HTML5, CSS, JavaScript (ES6+), Chromium/Electron/OpenFin, FDC3, REST/WebSocket APIs, Kafka/Solace/Tibco, and CI/CD with Jenkins. The team delivers pricing solutions and trading GUIs for Citi’s Foreign Exchange business. You will provide technical leadership, mentor junior engineers, and drive AI integration.

Lead platform engineering for internal shared data and AI capabilities, including ingestion, integration, canonical APIs, and agent workflow platforms with orchestration, monitoring, evaluation, observability, and guardrails. The team builds scalable, reliable platform services for LexisNexis Intellectual Property Solutions, enabling data-driven innovation and production-grade AI workflows across multiple product domains.

You will work with Python, RDKit, Biopython, OpenMM, MDAnalysis, docking, virtual screening, SAR, pharmacophore, QSAR, ADMET, and protein-ligand modeling tools. You will guide, evaluate, and improve AI-driven drug discovery workflows—reviewing agent-generated optimization plans, compound prioritizations, and modeling workflows—and lead computational chemistry strategy for live drug programs.

You will work with OpenMM, OpenFE, molecular dynamics, MM/GBSA, free-energy perturbation (RBFE/ABFE), and GPU infrastructure to define and validate automated biophysics tools. The team builds a scalable platform for agentic drug discovery workflows, ensuring scientific correctness in molecular simulation.

You will use PyTorch, JAX, and scikit-learn to build ML models in low-data regimes for drug discovery and molecular optimization. You will develop active learning, meta-learning, and uncertainty-aware approaches, designing data acquisition strategies that guide experiments, simulations, and compound selection.

The role involves training next-generation cofolding models using PyTorch, JAX, Boltz, AlphaFold-style methods, equivariant GNNs, contrastive learning, self-supervised representation learning, and DEL datasets. The scientist collaborates with ML researchers, chemists, biophysicists, and data engineers to improve protein-ligand binding prediction and AI-driven drug discovery. Lila Sciences builds an AI-powered operating system for autonomous scientific discovery across medicine, materials, and energy.

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.

Salesforce★4.2 (1.5k)· London

You will sell Agentforce, Data Cloud, and AI-powered CRM solutions as a Strategic Account Executive on Salesforce’s Enterprise High Tech team. The role focuses on leading complex, multi-million-pound transformational sales cycles and displacing legacy competitors within a portfolio of technology accounts.

Bloomberg★3.8 (1.1k)· London

Define strategy for AI Platform Experiences & Integrations within Bloomberg’s CTO Office. Work with UX and engineering teams on generative AI, agent systems, and emerging interfaces (MCP, A2A, A2UI). Product teams interact with these platforms through agent-driven experiences, copilots, and workflow orchestration surfaces.

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.

Use Python, SQL, BM25, vector search, reranking, and RAG pipelines to evolve Kharon's Corestream risk intelligence feed—a platform surfacing entity-level insights and topic-based risk narratives, integrating graph-based outputs into semantic search and narrative generation.

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.

You'll work with TypeScript, React, SQL, Amplitude, GrowthBook, PostHog, LaunchDarkly or Statsig, and Customer.io or Braze. The company is building a senior AI employee for small-business owners that drafts client work from existing tools. You own the activation funnel, experiment framework and retention loops as the first growth engineer on a small senior team.

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, SQL, BM25, vector search, reranking, RAG pipelines, agentic systems, and graph-aware search. Own and evolve Corestream, Kharon’s risk intelligence feed that surfaces entity-level insights and topic-based risk narratives for global security clients.

Deloitte★3.9 (14k)· London

Design, build and deploy intelligent agents and automations using Microsoft Copilot Studio, Power Automate, Power Apps, Dataverse and Microsoft 365. You will work within Deloitte Tax & Legal’s Digital team to create production-ready, low-code solutions that improve efficiency and quality in professional services delivery.

The technical lead will work with Java/J2EE, RESTful web services, Spring Boot, microservices, cloud technologies, Docker, Kubernetes, and CI/CD pipelines. They oversee architecture, design, and development of Java/J2EE applications, lead a team of developers, and drive technical excellence and best 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.

You'll work with Python, APIs, MCP servers, AI harnesses, agentic operating systems, and enterprise SEO/visibility toolsets. This role extends Jellyfish's live agentic delivery stack internally and helps enterprise clients build technical foundations for agent-driven search and GEO.

Lead a disputes team at Checkout.com, working with a .NET/C# back end, React/TypeScript front end, and Python for machine learning, alongside REST APIs, event-driven microservices, and SQL. The team builds tools and models to help merchants manage and win chargeback disputes at scale. You will oversee engineers across disciplines and seniority levels, driving predictable delivery and operational efficiency.

You'll use PHP, Laminas, Mezzio, Stripe, and AI tools like Claude Code. You'll build a new payments product and maintain existing payment integrations for Ticket Tailor's event ticketing platform. The team consists of two engineers alongside a Product Manager.

You'll work with PHP, Laminas, Mezzio, AI tools like Claude Code, Cloudflare, GitHub Actions, Datadog, and Amplitude. You'll build a new payments product and extend existing payment integrations for Ticket Tailor, enabling event organisers to get paid. You'll be part of a small team with two engineers alongside a Product Manager.

You’ll build customer-facing treasury software using **Python, React and TypeScript**. You’ll own features end to end for TreasurySpring’s Fixed-Term Fund platform, which replaces manual cash-management processes and legacy systems for institutions.