Data Scientist Lead
We have an opportunity to impact your career and provide an adventure where you can push the limits of what`s possible.
As a AI/ML Lead Engineer at JPMorgan Chase within Asset and Wealth Management, you will have the opportunity to work on state-of-the-art Language Modelling projects that will transform the operating model for the Wealth Management business. The key focus areas within this position will be on prompt engineering for Large Language Models (LLMs), and supporting the development and deployment of prompt-based models for various NLP tasks such as text classification, question answering, and language generation on a rich universe of financial and market data including unique datasets which can be leveraged to define the experiences for our clients and employees across the globe. At JPMorgan, you will be part of a world-class analytics community dedicated to skill development and career growth in analytics, data science, machine learning and beyond.
Job responsibilities
- Work with product managers, data scientists, ML engineers, and other stakeholders to understand requirements.
- Design, develop, and deploy state-of-the-art AI/ML/LLM/GenAI solutions to meet business objectives.
- Develop and maintain automated pipelines for model deployment, ensuring scalability, reliability, and efficiency.
- Conduct thorough evaluations of generative models (e.g., GPT-4), iterate on model architectures, and implement improvements to enhance overall performance in NLP applications.
- Implement monitoring mechanisms to track model performance in real-time and ensure model reliability.
- Stay informed about the latest trends and advancements in the latest AI/ML/LLM/GenAI research, implement cutting-edge techniques, and leverage external APIs for enhanced functionality.
- Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Strong understanding and hands-on experience in applied AI/ML engineering, with a track record of developing and deploying business critical machine learning models in production.
- Proficiency in programming languages like Python for model development, experimentation, and integration with Azure OpenAI API.
- Strong experience with machine learning frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit-learn, and Langchain/Llamaindex.
- Experience with cloud computing platforms (e.g., AWS, Azure, or Google Cloud Platform), containerization technologies (e.g., Docker and Kubernetes), and microservices design, implementation, and performance optimization.
- Solid understanding of fundamentals of statistics, machine learning (e.g., classification, regression, time series, deep learning, reinforcement learning), and generative model architectures, prompting strategies, RAG
- Ability to identify and address AI/ML/LLM/GenAI challenges, implement optimizations and fine-tune models for optimal performance in NLP applications.
Preferred qualifications, capabilities, and skills
- Familiarity with the financial services industries.
- Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts,
- A portfolio showcasing successful applications of generative models in NLP projects, including examples of utilizing OpenAI APIs.
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