AI platform engineer in financial services

Tradeweb is a global leader in electronic trading across asset classes. As financial markets become increasingly interconnected, our technology enables efficient, multi-asset trading on a global scale. We serve more than 3,000 clients in more than 85 countries, including many of the world’s largest banks, asset managers, hedge funds, insurers, corporations, and wealth managers.

• Develop and operate the shared AI platform on AWS Bedrock and AgentCore, supporting secure, reliable deployment of AI applications and agents.
• Maintain a shared model gateway with access controls, approved model configurations, usage monitoring and cost controls.
• Implement security, privacy and responsible AI controls, including agent identities, delegated authorization and auditable access, in line with Security Architecture standards.
• Build secure MCP integrations and RAG pipelines connecting agents to internal data and APIs, and support approved agent interoperability and tool registries.
• Use infrastructure as code and CI/CD to deploy, configure and maintain AI platform services consistently.
• Establish evaluation and observability to track output quality, agent behaviour, reliability, performance and cost.
• Provide reusable reference architectures, templates and secure AI coding tool configurations that help teams build and adopt AI solutions.
• Work hands-on with Risk, Cyber Security and Internal Audit to turn workflows into working AI solutions, partnering with Compliance and Legal on relevant requirements.

• Hands-on experience building and operating production LLM / generative AI platforms. Amazon Bedrock / AgentCore experience is preferred; comparable platforms such as Microsoft Azure AI Foundry or Google Vertex AI are welcome.
• Strong software engineering background with proficiency in Python and experience working with LLMs and generative AI.
• Experience connecting AI applications and agents to tools, APIs and internal data through MCP or comparable integrations, including retrieval-augmented generation (RAG).
• Experience managing model access and cost, and evaluating and monitoring AI services for quality, reliability and performance.
• Practical understanding of prompt injection, data leakage and excessive agency, with experience applying access controls, secrets management and auditing.
• Experience deploying AI services using cloud infrastructure, identity and access management, infrastructure as code and CI/CD. AWS experience is strongly preferred.
• Ability to turn internal workflows into working AI solutions, communicate clearly and create reusable components for wider adoption.
• Fluent in AI-assisted development, with hands-on experience configuring and standardizing tools such as Claude Code, Cursor or OpenAI Codex for safe team use.
• Будет плюсом: A2A protocols, delegated authorization, OAuth / OIDC and scoped agent credentials; Experience with LLM frameworks and evaluation or observability tools, such as LangChain, LangGraph, LlamaIndex, Strands Agents, provider SDKs, Langfuse, OpenTelemetry, CloudWatch or Ragas, or equivalents; Experience curating approved MCP server and agent skills registries; Familiarity with the OWASP Top 10 for LLM Applications and applying its guidance; Experience delivering technology in financial services, particularly alongside Risk, Cyber Security, Compliance or Internal Audit; Relevant certifications or AI-focused training, such as AWS Solutions Architect – Professional or AWS Machine Learning Engineer – Associate

• We offer a comprehensive range of benefits to support our employees at every stage of life and career. Our programs include enhanced parental leave, family-building and postpartum support through Maven, subsidized gym membership and a wide range of learning and development opportunities, to name a few!