Lead Software Engineer - AI/ML

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. 

As a Software Engineer III at JPMorgan Chase within the Consumer and community banking- Data technology, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives

Job responsibilities

• Lead the design and development of our AI/ML platform, ensuring robustness, scalability, and high performance.
• Drive the adoption of best practices in software engineering, machine learning operations (MLOps), and data governance.
• Ensure compliance with data privacy and security regulations relevant to AI/ML solutions.
• Maintain consistent code check-ins every sprint to ensure continuous integration and development.
• Enable the Gen AI platform and implement the  Gen AI Use cases ,LLM finetuning and multi agent orchestration
• Communicate technical concepts and solutions effectively across all levels of the organization.

Required Qualifications, Capabilities, and Skills

• Formal training or certification on software engineering concepts and 5+ years of applied experience
• Extensive practical experience with AWS cloud services, including EKS, EMR, ECS, and DynamoDB.
• Experience in Databricks ML lifecycle development.
• Advanced knowledge in software engineering, AI/ML, machine learning operations (MLOps), and data governance.
• Demonstrated prior experience in leading complex projects, including system design, testing, and ensuring operational stability.

• Expertise in computer science, computer engineering, mathematics, or a related technical field.

   

Preferred qualifications, capabilities, and skills

• Real-time model serving experience with Seldon, Ray, or AWS SM  is a plus.
• Understanding of large language model (LLM) approaches, such as Retrieval-Augmented Generation (RAG) and agent-based models, is a plus.