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Which ATS platforms does swipeandhired support?

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Is my data used for training AI models?

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Software Engineer III - AI/ML Developer at JPMorgan Chase, Plano, TX

Software Engineer III - AI/ML Developer at JPMorgan Chase in Plano, TX. Est. up to $200k. Apply with swipeandhired.

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 JPMorganChase within the Consumer & Community Banking, you are part of an agile team that works to enhance, design, and deliver the software components of the firm’s state-of-the-art technology products in a secure, stable, and scalable way. As an emerging member of a software engineering team, you execute software solutions through the design, development, and technical troubleshooting of multiple components within a technical product, application, or system, while gaining the skills and experience needed to grow within your role.

Job responsibilities:

Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems

Develops secure and high-quality production code, and reviews and debugs code written by others

Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.

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.

Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems

Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture

Designs, develops, codes, and troubleshoots with consideration of upstream and downstream systems and technical implications

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

Applies technical troubleshooting to breakdown solutions and solve technical problems of basic complexity

Gathers, analyzes, and draws conclusions from large, diverse data sets to identify problems and contribute to decision-making in service of secure, stable application development

Learns and applies system processes, methodologies, and skills for the development of secure, stable code and systems

Required qualifications, capabilities, and skills:

Formal training or certification on software engineering concepts and 3+ years applied experience

Hands-on practical experience delivering system design, application development, testing, and operational stability

Hands-on experience building, deploying, and maintaining machine learning platforms or infrastructure

Proficiency in Python and one or more ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Experience with data processing frameworks and tools (e.g., Spark, Pandas, SQL)

Practical experience with cloud-based ML platforms (e.g., AWS SageMaker, GCP AI Platform, Azure ML) or on-prem ML infrastructure

Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.

Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices

Proficient in all aspects of the Software Development Life Cycle

Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security

In-depth knowledge of the financial services industry and their IT systems

Practical cloud native experience

Preferred qualifications, capabilities, and skills:

Familiarity with Databricks for scalable data engineering and ML platform integration

Experience working with Snowflake for cloud-based data warehousing and analytics

Exposure to Snorkel AI for programmatic data labeling and training data management

Experience with containerization and orchestration tools (e.g., Docker, Kubernetes, Airflow)

Familiarity with feature stores, model registries, and ML metadata management

Experience with infrastructure-as-code tools (e.g., Terraform, CloudFormation)

Experience with RESTful APIs and microservices architectures