Software Engineer III

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 Corporate Technology Consumer and Community Banking Risk Technology team, 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. You will be responsible for executing software solutions, design, development, and technical troubleshooting with the ability to think beyond routine or conventional approaches to build solutions or break down technical problems. Additionally, you will work with product managers, data scientists, ML engineers, and other stakeholders to understand requirements and develop API/services for model deployment, ensuring scalability, reliability, and efficiency. Building applications to automate manual steps in MLOPs pipeline, executing POC for innovative ideas to solve complex business problems, and staying informed about the latest trends and advancements in the latest LLM/GenAI research are also part of your responsibilities. Furthermore, you will add to the team culture of diversity, equity, inclusion, and respect. Required qualifications, capabilities, and skills include formal training or certification on software engineering concepts and 3+ years of applied experience, proficiency in programming languages like Python for model development and Python for BigData (i.e., Pyspark, Spark SQL on EMR experience). Experience in web applications/APIs with AWS Lambda, cloud gateway, experimentation, and integration with OpenAI API, building API and restful services using Flask/Django/FastAPI, solid understanding of agile methodologies like CI/CD, Application Resiliency, and Security, experience with cloud computing platforms (e.g., AWS, Azure, or Google Cloud Platform), containerization technologies (e.g., Docker and Kubernetes), and microservices design, implementation, and performance optimization, as well as understanding the fundamentals of machine learning and LLMs, and experience in applied AI/ML engineering, with a track record of deploying business-critical machine learning models in production. Preferred qualifications, capabilities, and skills include familiarity with modern front-end technologies and the financial services industries.,