Be part of a dynamic team where your distinctive skills will contribute to a winning culture and team.

As a Applied AI ML Senior Associate at JPMorgan Chase within the Asset & Wealth Management, you serve as a seasoned member of an agile team to design and deliver trusted data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. You are responsible for developing, testing, and maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

• Involves in building and operating highly sophisticated LLM driven applications.
• Partners directly with other technology teams on LLM projects to advise and assist as needed.
• Collaborates with Data Science, Cybersecurity to deliver state of the art ML products.
• Collaborates with Devops engineers to plan and deploy data storage and processing systems,
• 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.
• Develops secure high-quality production code, and reviews and debugs code written by others.
• Stays abreast of the latest advancements in AI technologies, and drive their integration into our operations.

Required qualifications, capabilities, and skills

• Formal training or certification on data science engineering concepts and 3+ years applied experience
• Advanced python programming skills. Proven experience in building and operating scalable ML-driven products.
• Azure and/or AWS Certifications ( Architect, Big Data, AI/ML ) . Hands on experience in Azure and AWS.
• Proficiency with cloud technologies like Kubernetes, Airflow.
• Experience working in a highly regulated environment.
• 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.

Preferred qualifications, capabilities, and skills

• Experience with financial data and data science.
• Experience in developing AI solutions using agentic frameworks.
• Experience fine-tuning LLMs with advanced techniques to enhance performance.
• Experience with prompt optimisation to improve the effectiveness of AI applications.
• Demonstrated ability to design and implement robust AI application architectures.