Manager - Forward Deployed Engineer (FDE) - Risk Consulting

• AI-Driven Risk Solutions: Lead the design, development, and deployment of AI/ML and Generative AI (LLM) solutions tailored for traded risk management, market risk frameworks, and trading systems.
• Rapid Prototyping & Deployment: Act as a "forward-deployed" engineer, working closely with clients to build proof-of-concepts (PoCs) and rapidly scale them into production-grade risk engines.
• Risk Framework Transformation: Evaluate clients' legacy risk management frameworks, trading strategies, and valuation systems to identify areas ripe for AI-driven automation, predictive modeling, and optimization.
• Advanced Analytics & Stress Testing: Formulate and implement advanced machine learning models and stress-testing scenarios to quantify potential losses in trading portfolios (Rates, FX, Credit, Equities, Structured Products) stemming from major market shifts.
• Automated Risk Reporting & Operations: Architect, automate, and manage the production of next-generation risk monitoring pipelines (daily PnL attribution, VaR fluctuations, limit monitoring) utilizing GenAI, advanced data engineering, and modern visualization tools.
• Bridge Business & Tech: Act as the translation layer between client Front Office traders, Risk Management executives, and internal/external AI/Data engineering teams to implement and refine risk controls.
• Regulatory & Industry Alignment: Stay abreast of industry trends, regulatory changes (e.g., FRTB), and emerging AI standards to ensure all deployed models are compliant, explainable (XAI), and robust.
• Business Development & Thought Leadership: Contribute to business development by showcasing live demos, writing technical whitepapers on AI in Risk, and pitching innovative AI consulting solutions to senior client stakeholders.
• Team Mentorship: Mentor and coach junior consultants and engineers, fostering a culture of continuous learning across quantitative finance and modern software engineering.

& Experience

Education & Experience:

• A bachelor’s or master’s degree in a highly quantitative field: Computer Science, Financial Engineering, Data Science, Mathematics, or Physics. A professional qualification such as CFA, FRM, or CQF is highly desirable.
• 6+ years of relevant experience spanning quantitative risk management, financial engineering, or machine learning engineering within global investment banks, top-tier securities houses, or leading consulting firms.

Technical Skills (Core FDE Requirements):

• Programming & Frameworks: Exceptional proficiency in Python and standard machine learning libraries (scikit-learn, PyTorch, TensorFlow). Strong SQL and database management skills are required.
• Generative AI & LLMs: Hands-on experience with LLMs, prompt engineering, Retrieval-Augmented Generation (RAG) frameworks (e.g., LangChain, LlamaIndex), and vector databases.
• Data & MLOps: Experience with data pipelines (ETL), cloud platforms (AWS, Azure, or GCP), and DevOps/MLOps practices (Git, Docker, CI/CD) for deploying models in secure enterprise environments.

Risk Domain Knowledge:

• Strong understanding of Market Risk and Traded Risk frameworks, including VaR, Expected Shortfall (ES), Stress Testing, Sensitivity Analysis, and regulatory standards (e.g., FRTB).
• Solid knowledge of financial products (FICC, equities, derivatives, complex structured products) and related pricing/valuation methodologies.
• Exposure to Counterparty Credit Risk, XVA, Liquidity Risk, or ALM/IRRBB is a significant plus.

Consulting & Leadership Skills:

• Proven experience in managing complex technical projects with multiple senior stakeholders.
• Excellent problem-solving skills, with a track record of transforming ambiguous business problems into structured technical solutions.
• Superb communication and presentation skills, with the ability to explain complex AI/ML concepts to non-technical, C-suite executives.
• Fluency in English and Mandarin is essential for client engagement in Hong Kong and the wider Asia-Pacific region.