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.