Assistant Vice President - Senior Quant Developer

Hsbc

6 - 7 years

20 - 25 Lacs

bengaluru

Posted:1 day ago| Platform:

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Skills Required

computer science testing tools machine learning performance testing regression analysis unit testing principal financial services monitoring python

Work Mode

Work from Office

Job Type

Full Time

Job Description

Design and Develop LLMs: Build, train, and fine-tune LLMs and generative AI models for real-world applications, focusing on tasks such as document understanding, information extraction, question answering, and multimodal representation learning.
State-of-the-Art Research: Stay at the forefront of AI advancements, particularly in NLP, multimodal learning, and generative AI. Research and implement novel techniques to improve model performance and scalability.
Model Deployment: Collaborate with engineering teams to deploy models into production environments, ensuring seamless integration with existing systems and processes.
MLOps Practices: Contribute to the development of MLOps toolkits to streamline the machine learning lifecycle, from model training to monitoring and maintenance.
Define Validation Metrics: Develop robust metrics and frameworks to evaluate model accuracy, reliability, and robustness, particularly for LLMs.
Conduct Validation Assessments: Perform rigorous testing, including regression analysis, unit testing, and performance benchmarking, to ensure models meet business and regulatory standards.
Explore and Establish LLMs as Judges: Develop and implement methodologies for using LLMs as evaluators to assess the quality and accuracy of other AI models, particularly in tasks like natural language processing and document understanding.
Documentation & Reporting: Maintain detailed documentation of model development, validation processes, and performance metrics to support AI governance and regulatory filings.
Collaborate with Governance Teams: Work closely with internal compliance and governance teams to ensure models adhere to organizational policies and regulatory requirements.

Requirements

Bachelor s, Master s, or Doctorate degree in Machine Learning, Natural Language Processing (NLP), Computer Science, Data Science, Statistics, or a related field.
Hands-on Experience with LLMs: Strong expertise in developing, fine-tuning, and deploying LLMs and generative AI models.
Programming Skills: Proficiency in Python, with experience in libraries such as PyTorch, TensorFlow, Hugging Face Transformers, and tools like LangChain or LlamaIndex.
NLP & Multimodal Learning: Deep understanding of NLP techniques, multimodal representation learning, and document intelligence (e.g., document classification, information extraction, layout analysis).
Validation & Testing: Familiarity with unit testing frameworks, performance testing tools, and statistical methods for model evaluation.
MLOps Knowledge: Understanding of machine learning lifecycle management, model monitoring, and deployment pipelines.
Proven experience in both developing and validating AI/ML models, particularly in the context of LLMs.
Experience with document understanding, generative AI, or multimodal models is a plus.

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