Business: AVP - AI Model Specialist
Open positions:1
Role Title: Senior Quant Dev
Global Career Band: 5
Location (Country / City ): India/Bangalore
Recruiter Name : Srinivasan S
Why join us?
- Impactful Work: Play a pivotal role in shaping the future of AI-driven solutions while ensuring compliance and governance.
- Innovative Environment: Work with cutting-edge technologies and collaborate with a global team of experts.
- Professional Growth: Opportunities to grow your expertise in LLMs, generative AI, and MLOps.
The Opportunity:
- We are seeking a highly skilled and detail-oriented AI Model Specialist with expertise in Large Language Models (LLMs) and hands-on engineering experience. In this role, you will play a critical part in both developing and validating AI models to ensure their accuracy, reliability, and compliance with regulatory standards. You will work closely with AI governance, data science, and engineering teams to build, fine-tune, and validate LLMs, while also contributing to the documentation and governance processes required for model stewardship.
- If you are passionate about cutting-edge AI technologies, have a strong technical background in machine learning, and enjoy the challenge of ensuring models meet both business and regulatory requirements, this role is for you.
What you’ll do:
Model Development & Engineering:
- 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.
Model Validation & Governance:
- 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.
What you will need to succeed in the role:
Education:
- Bachelor’s, Master’s, or Doctorate degree in Machine Learning, Natural Language Processing (NLP), Computer Science, Data Science, Statistics, or a related field.
Technical Expertise:
- 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.
Experience:
- 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.
- Ability to work independently and collaboratively in a fast-paced, global environment.
Soft Skills:
- Strong analytical thinking and problem-solving skills.
- Attention to detail and excellent communication skills, with the ability to explain technical concepts to non-technical stakeholders.
- Ability to work under pressure, prioritize tasks, and deliver results in a dynamic environment.
Experience:
- 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.
- Ability to work independently and collaboratively in a fast-paced, global environment.
What additional skills will be good to have?
Cross-Functional Collaboration:
- Partner with data scientists, engineers, and business stakeholders to align model development with business objectives.
- Collaborate with testing engineers to design and execute test cases, ensuring model accuracy and reliability.
- Provide technical guidance and insights to non-technical stakeholders to facilitate informed decision-making.
Link to Candidate User Guide:
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You’ll achieve more at HSBC
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