Top Python Jobs in London

Work alongside full-time ML researchers on end-to-end projects using large market datasets and massive compute. Tasks include model design, implementation in Python and ML frameworks, hyperparameter tuning, debugging training issues, and analyzing model predictions for systematic trading challenges in nonstationary multi-agent environments.

Work alongside salespeople and sales-traders to support institutional client engagement across asset classes. Learn how client inquiries are handled, service clients and issuers, attend internal training on finance and trading fundamentals, and develop analytical, communication, and relationship-building skills in a fast-paced environment.

As a P&C Actuary, you will assess risks, review rate filings, prepare actuarial analysis, and communicate findings to clients and teams.

The Curriculum Manager - Cloud will oversee content development for cloud education, collaborating with experts and using AI tools to enhance course creation and engagement.

As a Curriculum Manager for Cloud, you will develop cloud education content, collaborate with experts, assess course performance, and leverage AI tools to improve quality.

As a Curriculum Manager, you'll create and manage cloud education content, collaborating with experts, utilizing AI tools, and addressing curriculum gaps.

The Curriculum Manager will oversee content development for cloud education, collaborating with experts to create engaging and technically accurate material while leveraging AI tools to enhance production and assess course performance.

Embed with strategic enterprise customers to architect, build, and deploy Alkira network solutions. Lead technical evaluations, design hybrid/public cloud networks, develop automation (Terraform, APIs, scripting), run workshops, troubleshoot, and drive adoption and product feedback across Sales, Engineering, and Product teams.

Build and deploy production-ready AI/ML systems across the full lifecycle, including agentic systems (RAG, prompt engineering), non-GenAI models (recommenders, forecasting), APIs/microservices, CI/CD and IaC, monitoring and guardrails, and cross-functional collaboration to ensure scalable, secure, and observable ML solutions.

Own and automate the GTM infrastructure: maintain CRM data quality, build enrichment/scoring workflows, detect buying signals, manage cold email deliverability, deploy AI agents and API integrations, run outbound campaigns and experiments, and partner with SDRs/AEs to scale revenue systems.

Analyze and investigate trading fraud cases, monitor trading activities to detect fraud, and improve fraud prevention processes.

Build and optimise scalable, serverless data pipelines and storage on GCP. Implement ETL, CI/CD and DataOps practices, support model productionisation with data scientists, document architectures, and guide migration toward event-driven managed cloud services.

Lead corporate actuarial activities including business planning, performance monitoring, and portfolio optimisation. Provide actuarial insight for pricing, reserving and strategy, develop tools and frameworks, collaborate with underwriting, risk and finance, ensure governance and compliance, and manage and develop a team.

Design, build, test, and operate scalable identity and authorization services for a global fintech platform. Lead architecture and code reviews, participate across the SDLC, maintain CI/CD best practices, and collaborate with cross-functional teams to deliver secure IAM and FGAC solutions.

Lead technical direction across platform squads, ship and operate critical platform services, improve CI/CD and delivery workflows, drive reliability (SLOs, observability, runbooks), remediate production issues, mentor engineers, and participate in on-call and incident response to raise platform quality and developer experience.

The Human Data Operations Strategist manages data annotation projects, optimizes workflows, ensures high data quality, and collaborates with clients and teams for AI model development.

Design, build and deploy machine learning and AI solutions end-to-end: data exploration, feature engineering, model selection, evaluation, deployment, maintenance, and tooling to scale ML across products.

Build and lead the Data Labeling team, define annotation guidelines and QA frameworks, partner with researchers to create labeling workflows, prioritize projects, monitor metrics, improve tooling and automation, manage vendors, run complex AI evaluation projects (RLHF, safety, benchmarks), and communicate insights to leadership.

Lead technical ML efforts to detect and prevent financial crime by designing, developing and shipping production deep learning, graph and sequence models. Provide IC technical leadership, mentor peers, work with product, engineering and MLOps to iterate models on a cloud-native platform, and measure production impact while minimising false positives and operational costs.

Design and build a centralized observability platform for satellite and deep-space networks. Implement metrics, logging, and tracing (Prometheus, Loki, Tempo/OpenTelemetry), define SLOs/SLIs/error budgets, automate deployments with IaC and GitOps, integrate observability into Kubernetes and cloud environments, support engineers with instrumentation and tooling, lead monitoring/alerting/incident response, and participate in on-call rotations.

Serve as the technical bridge between physical-layer communications and software orchestration: build and maintain Spacetime system models, write production adapter layers (Python/Go), design data schemas (Protobuf/JSON/gRPC), perform link-budget and propagation analyses, lead tier‑3 triage for live networks, and collaborate with product and engineering to convert integrations into reusable features.

Design, build and maintain modular, scalable, performant backend systems across the full software lifecycle; diagnose and resolve system issues; mentor junior engineers; collaborate with product and cross-functional teams; iterate quickly to deliver customer-focused features.

Contract Databricks Engineer to design and deliver cloud data lakehouse solutions (AWS/Azure), lead and mentor data engineers, run hands-on development (SQL/Python/PySpark), drive migrations and medallion architecture implementations, engage clients, shape technical strategy and support delivery and pre-sales.

Analyze large financial datasets, validate and develop automated models, model corporate actions and index changes, write client-facing research reports, communicate insights to clients, and source public information.

Build and scale AI-driven automation solutions using LLMs and agent frameworks. Design multi-step workflows, integrate AI with internal systems (HR, finance, legal, ticketing), automate document processing and data extraction, and train internal teams. Collaborate with technical leads, product, and IT to deliver practical, production-ready automation and iterate quickly.