Lead Prompt Engineer (KDB+ Real-Time Equity Trading Systems)

Job Description : Key responsibilities

Lead the design and development of effective prompts for AI models, focusing on applications within equity trading, real-time order monitoring, and advanced analytics.

Utilize KDB+ and q for processing and analyzing high-volume, high-frequency time-series data streams from sources like market feeds, facilitating actionable insights.

Collaborate with traders, quants, and analysts to understand business requirements and translate them into robust prompt engineering solutions for real-time order monitoring, trade reconciliation, and portfolio attribution analysis.

Design prompts for tasks such as data cleaning, transformation, automated reconciliation, and report generation and analysis within investment operations.

Optimize prompt performance, refine prompt logic, and troubleshoot issues to ensure accurate, relevant, and timely outputs from AI systems.

Develop systematic prompt evaluation methodologies and establish best practices for prompt creation, documentation, and version control.

Stay abreast of advancements in large language models (LLMs), prompt engineering techniques, AI / machine learning frameworks, and their potential application in financial markets.

Ensure prompt design and AI outputs adhere to regulatory and compliance requirements within the financial industry.

Required skills and qualifications

9+ years of hands-on experience in prompt engineering, with a strong focus on real-time applications.

Deep expertise in KDB+ development and q programming, including designing and implementing KDB+ architectures, and scaling systems in production environments.

Proven track record in developing and optimizing prompt engineering solutions for complex workflows, ideally within equity trading, order monitoring, and analytics.

Proficiency in programming languages like Python, with experience in AI / machine learning frameworks (e.g., TensorFlow, PyTorch).

Strong understanding of Natural Language Processing (NLP) and Large Language Models (LLMs), including techniques like few-shot learning and chain-of-thought prompting.

Experience in integrating AI models with external data sources and tools, including API scripting and vector embedding integration for retrieval-augmented generation (RAG).

Demonstrated technical leadership, problem-solving abilities, and decision-making in fast-paced projects.

Excellent communication skills, both written and verbal, capable of explaining complex systems to technical and non-technical stakeholders.

• Experience in capital markets or investment banking, with an understanding of financial instruments and market dynamics.