at J.P. Morgan
Bulge Bracket Investment BanksPosted 13 days ago
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**Software Engineer III - Python/AI/MLOps at JPMorganChase** Design and deliver secure, scalable AI/ML solutions using Python, Apache Spark, and Databricks. Collaborate to productionize models and modernize workflows. Provision and manage infrastructure as code using Terraform. Monitor platforms and optimize performance. 5+ years' experience required, with 3+ years in software engineering and proficiency in relevant tech stack.
- Compensation
- Not specified
- City
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- Country
- United States
Currency: Not specified
Full Job Description
Location: Wilmington, DE, United States
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 firms business objectives.
Job responsibilities
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 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 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
