Software Engineer III

As a Software Engineer III at JPMorgan Chase within the Consumer and Community Banking - Wealth Management, you will be a seasoned member of an agile team, tasked with designing and delivering trusted, market-leading technology products in a secure, stable, and scalable manner. You will be responsible for implementing critical technology solutions across multiple technical domains, supporting various business functions to achieve the firms business objectives.

Job responsibilities

• Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
• Creates secure and high-quality production code, maintaining algorithms that integrate seamlessly with generative AI systems and other appropriate technologies.
• Produces architecture and design artifacts for complex generative AI applications, ensuring design constraints are met by software code development.
• Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets to continuously improve generative AI applications and systems.
• Proactively identifies hidden problems and patterns in data, using these insights to drive improvements in coding hygiene and system architecture specific to generative AI.
• Contributes to software engineering communities of practice and events that explore new and emerging generative AI technologies.
• Fosters a team culture of diversity, equity, inclusion, and respect.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 3+ years applied experience.
• Proficient in coding in one or more languages, with a strong emphasis on Python and AI/ML frameworks.
• Experience in developing, debugging, and maintaining code in a large corporate environment, utilizing modern programming languages and database querying languages.
• Comprehensive knowledge of the Software Development Life Cycle, with a focus on generative AI applications.
• Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security, particularly in the context of AI/ML.
• Demonstrated knowledge of software applications and technical processes within technical disciplines such as cloud computing, artificial intelligence, machine learning, and mobile technologies.
• Expertise in AWS and GenAI/ML technologies.

Preferred qualifications, capabilities, and skills

• Experience with AI model optimization and performance tuning to ensure efficient and scalable AI solutions.
• Familiarity with data engineering practices to support AI model training and deployment.
• Ability to collaborate with cross-functional teams to integrate generative AI solutions into broader business processes and applications.
• Strong understanding of machine learning algorithms and techniques, including supervised, unsupervised, and reinforcement learning.
• Experience with AI/ML libraries and tools such as TensorFlow, PyTorch, Scikit-learn, and Keras.

As a Software Engineer III at JPMorgan Chase within the Consumer and Community Banking - Wealth Management, you will be a seasoned member of an agile team, tasked with designing and delivering trusted, market-leading technology products in a secure, stable, and scalable manner. You will be responsible for implementing critical technology solutions across multiple technical domains, supporting various business functions to achieve the firms business objectives.

Job responsibilities

• Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
• Creates secure and high-quality production code, maintaining algorithms that integrate seamlessly with generative AI systems and other appropriate technologies.
• Produces architecture and design artifacts for complex generative AI applications, ensuring design constraints are met by software code development.
• Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets to continuously improve generative AI applications and systems.
• Proactively identifies hidden problems and patterns in data, using these insights to drive improvements in coding hygiene and system architecture specific to generative AI.
• Contributes to software engineering communities of practice and events that explore new and emerging generative AI technologies.
• Fosters a team culture of diversity, equity, inclusion, and respect.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 3+ years applied experience.
• Proficient in coding in one or more languages, with a strong emphasis on Python and AI/ML frameworks.
• Experience in developing, debugging, and maintaining code in a large corporate environment, utilizing modern programming languages and database querying languages.
• Comprehensive knowledge of the Software Development Life Cycle, with a focus on generative AI applications.
• Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security, particularly in the context of AI/ML.
• Demonstrated knowledge of software applications and technical processes within technical disciplines such as cloud computing, artificial intelligence, machine learning, and mobile technologies.
• Expertise in AWS and GenAI/ML technologies.

Preferred qualifications, capabilities, and skills

• Experience with AI model optimization and performance tuning to ensure efficient and scalable AI solutions.
• Familiarity with data engineering practices to support AI model training and deployment.
• Ability to collaborate with cross-functional teams to integrate generative AI solutions into broader business processes and applications.
• Strong understanding of machine learning algorithms and techniques, including supervised, unsupervised, and reinforcement learning.
• Experience with AI/ML libraries and tools such as TensorFlow, PyTorch, Scikit-learn, and Keras.