Lead Prompt Engineer

7 days ago

Bengaluru, Karnataka, India NTT DATA Business Solutions Full time ₹ 12,00,000 - ₹ 36,00,000 per year

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.

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