JP Morgan Asset Management is expanding AI/ML use cases across AM business areas. We are seeking a software engineer with expertise in python, building data pipelines and prior experience in engineering AI/ML applications including LLMs. As an ML Engineering Lead within Asset Management you will be collaborating closely with various teams to prototype, build, test and deploy a large scale ML applications.

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

Hands on experience with data processing and pipeline architectures

• Hands on involvement in building and operating highly sophisticated ML driven applications.
• Experience with LLMS and prompt engineering
• Partnering directly with other technology teams on ML projects to advise and assist as needed.
• Collaborating with Data Science, Cybersecurity to deliver state of the art ML products.
• 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.
• Add to team culture of diversity, equity, inclusion, and respect.

Required qualifications, capabilities, and skills

• Advanced python programming skills including building reusable libraries.
• Formal training or certification on software engineering concepts and advanced applied experience.
• Proven experience in building and operating scalable ML-driven products
• AWS experience (Certifications optional) (Architect, Big Data, AI/ML)
• Hands on experience in Azure and AWS and proficiency with cloud technologies like Kubernetes, Airflow.
• Experience working in a highly regulated environment
• Proven ability to iterate quickly.
• Proficient in all aspects of the Software Development Life Cycle.
• Terraform, IaaC experience.
• Experience with design & delivery of large scale cloud-native architectures.
• Experience with microservices performance tuning, performance optimization, real-time applications.