AI Developer / Quant Developer

About the job

Project Description: • Lead the design and development of advanced quantitative and AI-driven models for market abuse detection across multiple asset classes and trading venues. Drive the solutioning and delivery of large-scale surveillance systems in a global investment banking environment, leveraging Python, PySpark, big data technologies, and MS Copilot for model development, automation, and code quality. Play a pivotal role in communicating complex technical concepts through compelling storytelling, ensuring alignment, and understanding across business, compliance, and technology teams. Responsibilities: • Architect and implement scalable AI/ML models (using MS Copilot, Python, PySpark, and other tools) for detecting market abuse patterns (e.g., spoofing, layering, insider trading) across equities, fixed income, FX, and derivatives. • Collaborate closely with consultants, MAR monitoring teams, and technology stakeholders to gather requirements, share insights, and co-create innovative solutions. • Translate regulatory and business requirements into actionable technical designs, using storytelling to bridge gaps between technical and non-technical audiences. • Develop cross-venue monitoring solutions to aggregate, normalize, and analyze trading data from multiple exchanges and platforms using big data frameworks. • Design and optimize real-time and batch processing pipelines for large-scale market data ingestion and analysis. • Build statistical and machine learning models for anomaly detection, behavioral analytics, and alert generation. • Ensure solutions are compliant with global Market Abuse Regulations (MAR, MAD, MiFID II, Dodd-Frank, etc.). • Lead code reviews, mentor junior quants/developers, and establish best practices for model validation and software engineering, with a focus on AI-assisted development. • Integrate surveillance models with existing compliance platforms and workflow tools. • Conduct backtesting, scenario analysis, and performance benchmarking of surveillance models. • Document model logic, assumptions, and validation results for regulatory audits and internal governance. Mandatory Skills Description: • 7+ years of experience • Investment banking domain experience • Advanced AI/ML modelling (Python, PySpark, MS Copilot, kdb+/q, C++, Java) • Must be well versed with SQL and have hands on experience writing SQL (preferably Spark SQL) that is productionized (not ad-hoc queries) for at least 2-4 years • Familiarity with Cross-Product and Cross-Venue Surveillance Techniques particularly with vendors such as TradingHub, Steeleye, Nasdaq or NICE • Statistical analysis and anomaly detection • Large-scale data engineering and ETL pipeline development (Spark, Hadoop, or similar) • Market microstructure and trading strategy expertise • Experience with enterprise-grade surveillance systems in banking. • Integration of cross-product and cross-venue data sources • Regulatory compliance (MAR, MAD, MiFID II, Dodd-Frank) • Code quality, version control, and best practices. • Strong storytelling and communication for technical and non-technical audiences • Collaboration with consultants, MAR monitoring teams, and technology stakeholders • Stakeholder management and requirements gathering • Leadership, mentoring, and team guidance • Problem-solving and critical thinking • Adaptability and continuous learning Nice-to-Have Skills Description: • Understanding of Financial Markets Asset Classes (FX, FI, Equities, Rates, Commodities & Credit), various trade types (OTC, exchange traded, Spot, Forward, Swap, Options) and related systems is a plus • Surveillance domain knowledge, regulations (MAR, MIFID, CAT, Dodd Frank) and related Systems knowledge is certainly a plus Languages: • English: Proficient

Requirements

• quantitative modeling
• AI-driven models
• Python
• PySpark
• big data technologies
• SQL
• surveillance systems
• anomaly detection
• regulatory compliance

Preferred Technologies

• quantitative modeling
• AI-driven models
• Python
• PySpark
• big data technologies
• SQL
• surveillance systems
• anomaly detection
• regulatory compliance