Software Engineer III - Java, AI, AWS at JPMorgan Chase

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 Commercial & Investment Bank, 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

• Design, develop, and implement AI and data engineering solutions, including data pipelines, machine learning models, analytics applications, and Agentic AI systems.

• Write secure, high-quality code in Python, applying best practices for AI and data engineering with limited guidance.

• Develop and maintain scalable data architectures and integrate AI models and autonomous agents into production systems, considering upstream and downstream technical implications.

• Utilize AWS services (e.g., S3, Redshift, SageMaker, Lambda) to build and deploy data and AI solutions.

• Troubleshoot and resolve technical issues related to data processing, model deployment, agent orchestration, and application stability.

• Learn and apply system processes, methodologies, and skills for the development of secure, stable code and AI/data systems.

• Support production systems, including monitoring, incident response, and continuous improvement for AI and data applications.

• Design, develop, and integrate Agentic AI solutions, such as autonomous agents, multi-agent systems, or AI-driven workflow automation, into existing and new products.

• Collaborate with cross-functional teams to identify opportunities for Agentic AI applications that enhance business processes, decision-making, and user experiences.

• Implement and monitor autonomous AI agents, ensuring reliability, safety, and alignment with organizational goals.

• Stay current with advancements in Agentic AI, reinforcement learning, and related frameworks, applying best practices to production systems.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 4+ years applied experience in software development, with deep expertise in AI and data engineering, AWS services, Python, and databases such as Redshift and Oracle.

• Proficiency in python is a must

• Experience with infrastructure-as-code tools like Terraform and familiarity with Github Copilot.

• Hands-on practical experience in application development, testing, and operational stability, especially for AI and data-driven solutions.

• Experience in developing, debugging, and maintaining code in a large corporate environment, including modern programming languages and database querying languages.

• Demonstrable ability to code in one or more languages and build data pipelines, machine learning models, analytics applications, or autonomous agents.

• Experience across the whole Software Development Life Cycle, including model training, deployment, and monitoring.

• Exposure to agile methodologies, CI/CD, application resiliency, and security best practices.

• Strong knowledge of software applications and technical processes within AI, machine learning, data engineering, and cloud disciplines.

• Hands-on experience or strong understanding of Agentic AI concepts, such as autonomous agents, multi-agent systems, or AI orchestration frameworks.

• Experience building or integrating AI agents for data processing, workflow automation, or decision support.

Preferred qualifications, capabilities, and skills

• Familiarity with modern front-end technologies and integrating AI/data solutions with user interfaces.
• Exposure to advanced cloud technologies and managed AI/data services (e.g., AWS Sage Maker, Azure ML).
• Experience with big data frameworks (e.g., Spark, Hadoop) and data orchestration tools (e.g., Airflow).
• Understanding of MLOps and data governance best practices.
• Familiarity with frameworks and libraries for Agentic AI (e.g., Lang Chain, Auto Gen, CrewAI, or similar).