Top Python Jobs in London

Lead end-to-end technical delivery of bespoke AI/LLM-based agent solutions on the Salesforce platform: design and implement architectures, build data integrations and pipelines, prototype and deploy production systems, troubleshoot complex issues, mentor junior engineers, and act as primary technical advisor to customers while driving Agentforce product improvements.

The Senior Data Engineer will deliver complex data engineering for insights and analytics, collaborating with multiple stakeholders and managing team outputs.

Own and evolve HTB’s internal platform stack, including identity and access management, Google Workspace, endpoint management, SaaS administration, security tooling, compliance automation, secrets management, and an AI/LLM gateway. Build integrations and workflow automations, maintain documentation and runbooks, support secure scaling, and participate in on-call for critical identity, email, and SaaS incidents.

Leads Market Access and HEOR consulting projects from planning through delivery, overseeing research design, evidence synthesis, statistical and modeling outputs, quality assurance, client engagement, and strategic deliverables. Manages cross-functional teams and direct reports, supports proposals and commercial growth, allocates resources, manages risks and budgets, and develops client relationships. Provides coaching, performance feedback, and methodological oversight across quantitative and qualitative projects.

Leads HEOR, market access, pricing, reimbursement, and evidence strategy projects for pharmaceutical and biotech clients. Responsibilities include overseeing research and evidence synthesis, interpreting quantitative and qualitative analyses, developing strategic recommendations, managing timelines and budgets, leading client presentations and workshops, supporting proposals and business development, ensuring quality control, and coaching junior team members. The role requires independent client engagement and strategic leadership across multiple projects and workstreams.

Develop, deploy, and maintain scalable AI/ML pipelines and production models, including LLMs and generative AI. Optimize model performance, latency, and cost across AWS, GCP, and Azure. Apply MLOps practices such as CI/CD, versioning, monitoring, logging, and maintenance. Collaborate with data scientists, software engineers, solution architects, and cross-functional teams to integrate AI capabilities into products and workflows.

In this role, you'll develop reliable Python systems for trading operations, collaborate on projects, debug issues, and provide operational support.

Build automated control-quality metrics, evaluation pipelines, and deterministic test infrastructure for humanoid robots. Develop simulation and hardware-in-the-loop testing integrated with CI, establish fleet-level performance benchmarks, track robot variation and degradation, and create dashboards for control health. The role requires robotics experience, strong Python, modern C++, data-analysis judgment, ROS/ROS2, synchronization, and test automation expertise.

Design and maintain scalable Java backend systems, REST APIs, microservices, and distributed architectures. Lead technical and architectural decisions, promote testing and automation, mentor engineers, collaborate with clients and cross-functional teams, and evaluate emerging technologies including AI to improve engineering delivery.

Build and maintain backend features for Agent Observability across the GitLab monolith and AI gateway. Design and implement GraphQL/REST APIs, improve automated tests (RSpec), collaborate cross-functionally, troubleshoot production issues on Tier 2 on-call, and help shape responsible AI integration and platform architecture.

Designs, deploys, maintains, and improves enterprise infrastructure, primarily private cloud and Linux environments. Responsibilities include architecture, connectivity, upgrades, patching, application and infrastructure support, business continuity and disaster recovery, critical incident response, and internal project delivery. The role emphasizes Infrastructure as Code, automation, DevOps tooling, cloud platforms, enterprise Linux administration, and continuous service availability improvements.

Designs, builds, and operates highly available multi-cloud infrastructure across AWS, Azure, Kubernetes, and on-premises environments. Owns networking, infrastructure-as-code, CI/CD, observability, disaster recovery, and SRE practices for a globally distributed trading platform. Develops AI-assisted operational workflows for incident response, automation, and toil reduction. Mentors senior engineers, serves as a cross-team design authority, and contributes to the platform roadmap in a regulated, availability-critical environment.

Own a platform domain end to end, defining architecture, roadmap, reliability, security, and cost standards. Build scalable multi-tenant Kubernetes infrastructure, enable self-service deployments, advance agent-native engineering practices, and develop tooling for safe agent execution. Partner with engineering teams to translate infrastructure needs into reusable capabilities, mentor engineers, lead design decisions, and manage on-call and post-incident processes.

Lead architecture and full-stack development of AI-powered platforms, build scalable frontend and backend systems, mentor engineers, translate client problems into production-ready solutions, and ensure high code quality and reliable deployments.

As a Machine Learning Engineer, you will develop software solutions using machine learning, collaborate with clients, and deliver impactful projects working within cross-functional teams.

The Senior Research Scientist will conduct high-impact AI safety research, lead evaluations and red-teaming projects, and support academic publication efforts in applied AI safety.

Design, build and deploy production-grade ML systems for retail and consumer clients. Lead technical scoping, architecture and best practices, collaborate with data scientists and engineers, operationalise models, and advise customers to deliver scalable, secure ML solutions that drive business outcomes.

The role involves developing and managing key components of Sphere's platform, such as the tax engine and filing infrastructure, while mentoring other engineers and improving efficiency based on customer feedback.

As an AI Consultant, you'll analyze data, develop AI models, guide junior analysts, and communicate results, focusing on innovative solutions and predictive modeling.

As an AI Deployment Strategist, you'll design and deploy AI-powered planning solutions, interact with clients, and bridge between technical and product teams to drive AI adoption.

Develop and refine NLP projects by improving LLM models, implementing production models, and conducting performance experiments.

Build and ship backend-leaning fullstack features (APIs, services, integrations, React frontend). Scope solutions, implement, and own features end-to-end. Collaborate with product and design, contribute to architecture and technical discovery, ensure scalability, reliability, and security best practices, and improve developer experience and tooling.

Provide senior advisory and technical guidance for a foundational enterprise data program in financial services. Govern data mart modeling, schema and semantic-layer decisions; establish data science, reproducibility, documentation and data quality standards; assess analytical readiness; sequence remediation; advise on AI governance; and upskill FP&A and data engineering teams.

Design and lead enterprise GenAI, conversational AI, and agentic AI architectures for Anaplan’s planning platform. Define architecture patterns, APIs, data models, prompt engineering frameworks, observability, evaluation, guardrails, and scalable production systems. Collaborate with product, engineering, and data science teams; evaluate AI technologies; lead architecture reviews; and mentor engineers delivering secure, reliable AI solutions for forecasting, scenario planning, and financial modeling.

Design, build, and evolve production-grade trading platforms and data-serving services. Optimize performance, throughput, and efficiency at scale while improving reliability, testing, automation, code quality, and observability. Participate in production support rotations, troubleshoot incidents, and drive operational improvements across distributed data systems.