JPMorgan Large Language Model LLM Engineering Lead - Vice President 

United States, New York, New York 

94286498

As a Large Language Model (LLM) Engineering Lead within Asset Management, you will be collaborating closely with various teams to prototype, build, test and deploy a large scale federated LLM platform. You will work with an agile team that will work on building, and delivering trusted market-leading technology products in a secure, stable, and scalable way. You will partner with our Global Data Science teams to design, develop, deploy and operate machine learning driven applications and data pipelines.

Job responsibilities

• Participate in building and operating highly sophisticated LLM driven applications.
• Partnering directly with other technology teams on LLM projects to advise and assist as needed.
• Collaborating with Data Science, Cybersecurity to deliver state of the art ML products.
• Managing and supporting a team of ML and MLOps engineers.
• Collaborating with Devops engineers to plan and deploy data storage and processing systems,
• Executing 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.
• Developing secure high-quality production code, and reviews and debugs code written by others.

Required qualifications, capabilities, and skills

• Degree in a computer science or related discipline.
• Formal training or certification on software engineering concepts and advanced applied experience.
• Advanced python programming skills.
• Proven experience in building and operating scalable ML-driven products.
• Hands on experience and certifications in Azure or AWS (Architect, Big Data, AI/ML).
• Proficiency with cloud technologies like Kubernetes or Airflow.
• Experience working in a highly regulated environment.
• Proficient in all aspects of the Software Development Life Cycle.
• Familiarity with Terraform or IaaC.
• Ability to design and deliver large scale cloud-native architectures.
• Expertise in microservices performance tuning, performance optimization, and real-time applications.

Preferred qualifications, capabilities, and skills

• Experience with financial data and data science.