
Software Engineer III - GenAI, AWS, Java/Python
at J.P. Morgan
Posted a month ago
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- Compensation
- Not specified
- City
- Jersey City
- Country
- United States
Currency: Not specified
Join JPMorgan Chase's Asset and Wealth Management division as a Software Engineer III focused on generative AI and full-stack development. You will design, build, and productionize agentic/GenAI solutions using Java or Python, front-end frameworks (React/Angular), and AWS, contributing to POCs and scalable architectures. The role emphasizes prompt engineering, automation, operational stability, and collaboration with cross-functional teams to integrate AI capabilities into business applications.
Location: Jersey City, NJ, United States
We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.
As a Lead Software Engineer at JPMorgan Chase within the Asset and Wealth Management division, you will be instrumental in promoting the progress and application of state-of-the-art AI technologies. Your role will focus on key development tasks, leveraging your technical expertise to enhance the design and implementation of pioneering key business features powered by Agentic/Gen AI frameworks.
Job responsibilities
- Generative AI Development: Contribute to the development and implementation of generative AI solutions, ensuring they meet technical requirements and business objectives.
- Technical Expertise: Demonstrate deep expertise in generative AI technologies, actively participating in the development of proofs of concept (POCs) and exploring new methodologies to enhance AI capabilities.
- Programming Proficiency: Exhibit strong proficiency in Java or Python, with the ability to architect and build complex AI models from scratch. Ensure the delivery of secure, high-quality production code.
- Front-End Development: Utilize experience with React or Angular to create intuitive user interfaces for AI applications. Conduct thorough code reviews to maintain high standards of code quality.
- Cloud Integration: Leverage AWS experience to implement best practices in AI integration, ensuring quality, security, and efficiency across AI projects.
- Automation and Stability: Identify and implement opportunities to automate processes and enhance the operational stability of generative AI applications and systems.
- Collaboration: Work closely with cross-functional teams to contribute to the evaluation and refinement of architectural designs for AI systems, ensuring scalability and robustness.
- Continuous Learning: Engage in communities of practice to stay updated on new generative AI technologies, fostering a culture of continuous innovation.
- Team Culture: Support a team culture of diversity, opportunity, inclusion, and respect, fostering an environment conducive to innovative AI development.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 3+ years applied experience
- Programming Proficiency: Minimum 3+ years of strong proficiency in Java or Python, with the ability to architect and build complex AI models from scratch.
- Generative AI: one year of experience in generative AI development and prompt engineering.
- System Design: Proven experience in system design, application development, testing, and operational stability in AI projects.
- Front-End Development: Minimum of 3+ years of strong experience with React or Angular.
- Cloud Expertise: 2+ years of AWS experience is a must-have.
- Tool Proficiency: Experience with LLM models and agentic tools.
- Agile Methodologies: Understanding of agile methodologies such as CI/CD, Application Resiliency, and Security, applied to AI projects.
- 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 Langchain, PyTorch, Scikit-learn, and Keras.




