Develop and productionize machine learning models and AI agents for financial-market intelligence and client workflows. Responsibilities include forecasting, sentiment analysis, market classification, LLM/RAG agent development, backtesting, hallucination testing, governance, human-review controls, provider benchmarking, regulatory compliance, and cross-functional collaboration. The role also provides technical leadership and mentorship while applying advances in AI and machine learning to financial markets.
In this role, you will play a pivotal part in developing and productionizing our proprietary ML models and AI agents, shaping the future of financial markets. Your expertise will be instrumental in enhancing our market intelligence and client workflows, ensuring we stay at the forefront of the industry.
Responsibilities- Develop ML models for various financial market regimes, forecasting, event risk assessment, sentiment analysis, and market classification.
- Construct LLM/RAG/tool-calling agents to enhance market intelligence and streamline client workflows.
- Oversee model evaluation, backtesting, hallucination testing, and release gates, ensuring the highest standards of accuracy and governance.
- Integrate quant signals, trusted data sources, and human-review controls for robust and reliable model performance.
- Benchmark model providers and explore open-source alternatives to stay ahead of the curve.
- Collaborate with cross-functional teams to align ML models and AI agents with business objectives and client needs.
- Provide technical leadership and mentorship to junior team members, fostering a culture of continuous learning and improvement.
- Stay updated with the latest advancements in AI/ML technologies and apply them to enhance our market intelligence capabilities.
- Ensure compliance with regulatory requirements and industry best practices in the development and deployment of ML models and AI agents.
- Strong proficiency in Python, with a solid understanding of ML fundamentals, including classification, clustering, time series analysis, and regime detection.
- Hands-on experience with LLM/RAG/agent technologies, demonstrating a track record of shipping models beyond notebooks.
- In-depth knowledge of financial markets, including macro, FX, rates, central banks, and economic data, with a keen understanding of market dynamics.
- Excellent judgment and decision-making skills, particularly in assessing model accuracy, governance, and the need for human-in-the-loop review.
- Experience in forecasting and nowcasting techniques is highly advantageous, as is expertise in financial NLP and fine-tuning.
- A background in quant finance or systematic strategies is desirable, providing a unique perspective on financial market modeling.
- Excellent communication and collaboration skills, with the ability to work effectively in a cross-functional team environment.
- A proactive and innovative mindset, constantly seeking opportunities to enhance our ML models and AI agents.
- A strong work ethic, attention to detail, and a commitment to delivering high-quality results.
Cantor Fitzgerald London, England Office
London, United Kingdom, 0
Similar Jobs
Fintech • Mobile • Payments • Software • Financial Services
Coordinate and execute risk-based internal audits across Finance and Treasury, including financial risk and regulatory obligations. Develop data-driven continuous monitoring and automated audit routines, assess governance and controls, track recommendation implementation, and collaborate with stakeholders across a multinational organization. The role also contributes to developing the internal audit methodology, team operations, and assurance framework.
Top Skills:
Automated Audit RoutinesContinuous MonitoringData Analytics
Fintech • Mobile • Payments • Software • Financial Services
Build governance and assurance foundations for Wise’s Risk Assessment platform. Develop data quality monitoring, contracts, lineage tracking, control validation, drift detection, audit trails, explainability tooling, and regulatory evidence generation. Integrate these capabilities with the Risk API and collaborate with Product and Financial Crime teams. The role requires designing high-throughput, reliable backend systems that make risk decisions trusted, explainable, auditable, and continuously monitored.
Top Skills:
JavaKafkaNoSQLSQL
Fintech • Mobile • Payments • Software • Financial Services
Lead data science initiatives for KYC and customer onboarding, developing and managing machine learning models for document verification. Responsibilities include creating large image and tabular datasets, iterating deep learning models, optimizing performance, maintaining Python code, researching fraud prevention techniques, collaborating with cross-functional teams, communicating results to stakeholders, and mentoring junior data scientists.
Top Skills:
Deep LearningMachine LearningPython
What you need to know about the London Tech Scene
London isn't just a hub for established businesses; it's also a nursery for innovation. Boasting one of the most recognized fintech ecosystems in Europe, attracting billions in investments each year, London's success has made it a go-to destination for startups looking to make their mark. Top U.K. companies like Hoptin, Moneybox and Marshmallow have already made the city their base — yet fintech is just the beginning. From healthtech to renewable energy to cybersecurity and beyond, the city's startups are breaking new ground across a range of industries.
