Software Engineer III - Python/AI/MLOps

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. As a Software Engineer III - Python/AI/MLOps at JPMorganChase within the Corporate 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. Job responsibilities Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness. Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation. Design, build, and support scalable machine learning platform capabilities on Databricks for risk and fraud model implementation Partner with model developers and engineers to productionize models using Python and Apache Spark, including data preparation and distributed processing patterns Modernize machine learning workflows from Amazon SageMaker to Databricks, improving reliability, speed, and standardization Build and maintain infrastructure as code using Terraform to provision and manage cloud resources across environments Implement monitoring, alerting, and operational controls using tools such as Amazon CloudWatch and Datadog to improve platform observability Tune performance of Databricks and Spark workloads through profiling, optimization, and best practices Develop automation scripts and tooling that reduce manual effort and improve operational stability Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and 3+ years applied experience Proficiency in Python and experience building data processing workloads with Apache Spark Hands-on experience developing on Databricks, including job configuration, libraries, and runtime management Experience operating workloads in Amazon Web Services (for example: Amazon EC2, Amazon S3, Amazon EMR, Amazon Lambda) Experience provisioning and managing cloud infrastructure using Terraform and automated deployment pipelines Experience with monitoring and alerting using tools such as Amazon CloudWatch and Datadog, plus strong communication and collaboration skills Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security. Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices. Preferred qualifications, capabilities, and skills Familiarity with modern front-end technologies Exposure to cloud technologies