You will join a high-caliber team responsible for modernizing our data infrastructure and deploying advanced models that drive efficiency and insight. You will have significant autonomy to select tools, design architectures, and influence the firm's broader AI roadmap.
Key Responsibilities- GenAI Development: Design and implement advanced RAG (Retrieval-Augmented Generation) architectures and fine-tuned models to unlock insights from proprietary financial data.
- Hybrid Cloud Engineering: Develop seamless integration between on-prem legacy systems and AWS cloud infrastructure using Python, Java, and Spark .
- Production Deployment: Transition experimental models into reliable, monitored production services, ensuring high availability and data consistency.
- Model Governance: Partner with Compliance and Risk teams to establish automated testing frameworks for model accuracy, hallucinations, and drift—ensuring all solutions are audit-ready.
- Market Leadership: actively monitor the AI/ML landscape to introduce new libraries, frameworks, and methodologies to the firm.
- Industry Background: 5+ years of engineering experience, with significant tenure at a major Investment Bank or Hedge Fund.
- Technical Stack: Proficiency in Python (PyTorch, TensorFlow, LangChain) and Java . Strong command of SQL and distributed computing (Spark).
- Architecture: Experience building event-driven architectures using Kafka within a financial context.
- Communication: Ability to explain complex AI concepts to non-technical stakeholders and investment professionals.
- Ethical Standards: Unwavering commitment to data privacy, ethics, and the regulatory standards of the financial services industry.
