AI/ML Lead

Company Description 

 
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. 

Creative collaboration and sharp client focus have helped fuel our organic growth. We facilitated average daily trading volume (ADV) of more than $2.8 trillion over the past four fiscal quarters, topping $3.3 trillion in ADV for the first quarter of 2026. 

Since our IPO in 2019, Tradeweb has completed four acquisitions and doubled our revenues – and 2025 was our 26th consecutive year of record revenues. 

Tradeweb plays a central role in modernizing market structure by developing innovative trading protocols, embedding analytics into execution, and building technology infrastructure that supports the convergence of traditional and digitally native financial markets. Tradeweb is a great place to work, recognized in 2025 by Forbes as one of America’s Best Companies and by U.S. News & World Report as one of the Best Financial Services Companies to Work For. 

Tradeweb Markets LLC ("Tradeweb") is proud to be an EEO Minorities/Females/Protected Veterans/Disabled/Affirmative Action Employer. 

 
Workplace Posters | U.S. Department of Labor 

Group Details 

We are seeking an experienced AI/LLM Engineer to design and build intelligent, language-driven agentic  systems that translate user intent into structured execution workflows and. This role sits at the intersection of large language models, data science, and electronic trading, with a focus on building scalable, accurate, and explainable AI-powered decision systems. 

Job Responsibilities 

• LLM Integration & Prompt Architecture 

• Design and own the integration between conversational AI agents and external large language model APIs 

• Develop and continuously refine prompt architectures that translate natural language into structured system actions and parameters 

• Build confirmation and validation flows to ensure outputs are accurate, explainable, and auditable 

• Establish and maintain high standards for interpretation accuracy at scale 

• Intent Parsing & Natural Language Processing 

• Build systems that reliably map user instructions into structured parameters across varied phrasing, domains, and strategies 

• Define evaluation frameworks to measure and improve NLP performance 

• Handle ambiguity, edge cases, and fallback scenarios while maintaining user trust 

• Intelligent Recommendation Systems 

• Design and develop engines that proactively recommend optimized configurations or actions based on historical behavior, real-time conditions, and benchmark data 

• Incorporate multiple data sources to generate insights that outperform manual decision-making 

• Continuously improve recommendation quality through feedback and iteration 

• Feedback Loops & Model Improvement 

• Build mechanisms that leverage post-action or post-trade analytics to refine system recommendations over time 

• Enable adaptive learning based on performance outcomes across different market or operational conditions 

• Benchmarking & Data-Driven Insights 

• Collaborate with data and quantitative teams to develop anonymized benchmarking models 

• Define statistical methodologies for performance comparison and optimization 

• Ensure privacy, aggregation, and data governance standards are met 

• Translate complex analytics into actionable outputs within AI-driven systems 

• Conversational Analytics 

• Enable natural language interaction with complex datasets 

• Build systems that interpret user queries, retrieve relevant data, and generate accurate, synthesized insights 

• Support use cases such as performance analysis, comparisons, and trend identification through conversational interfaces 

• Chatbot deployment management 

• Deliver product demonstrations to clients and internal teams 

• Manage user access provisioning and provide technical onboarding support 

• Advise clients on prompt engineering best practices to maximise platform value 

• Gather, document, and prioritise client feedback to inform AI chatbot enhancements, including direct resolution of issues where possible 

Qualifications 

• 5+ years of experience in applied ML or AI engineering, with at least 2 years working directly with large language models in production 

• Deep hands-on experience with LLM integration — prompt engineering, tool use, structured output, evaluation frameworks, and hallucination mitigation 

• Strong Python — this is the primary development language for the agent layer and the SDK 

• Experience building systems that translate unstructured natural language into structured, actionable outputs in a production environment 

• Comfort working in a regulated industry where every model output needs to be explainable and auditable 

• Ability to own a technical workstream end-to-end — architecture decisions, implementation, and quality bar