Software Engineer III - AI/ML Developer

JPMorganChase•Plano, TX

About The Position

As a Software Engineer III in JPMorganChase’s Consumer & Community Banking organization, you will join an agile team that enhances, designs, and delivers secure, stable, and scalable software components for the firm’s technology products. You will design, develop, test, and troubleshoot multiple components of technical products, applications, or systems, while building your skills and experience within the role. The role emphasizes developing and maintaining machine-learning platforms or infrastructure, applying approved AI-assisted engineering practices, and improving software quality, delivery speed, and operational outcomes. You will help establish validation standards for AI-generated work, including secure coding, peer review, and automated testing, and promote reuse of effective practices across the team. You will also work with large and diverse data sets, assess technical designs, and consider the implications of upstream and downstream systems.

Requirements

Formal training or certification in software engineering concepts and at least 3 years of applied experience.
Hands-on 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 machine-learning frameworks, such as TensorFlow, PyTorch, or Scikit-learn.
Experience with data processing frameworks and tools such as Spark, Pandas, or SQL.
Practical experience with cloud-based machine-learning platforms, such as AWS SageMaker, GCP AI Platform, or Azure ML, or with on-premises ML infrastructure.
Demonstrated experience leading effective use of approved AI-assisted software-development tools for activities such as coding, code review, test acceleration, or troubleshooting, including setting expectations for validating AI outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, and adherence to resiliency and security expectations.
Proficient in all aspects of the Software Development Life Cycle.
Advanced understanding of agile methodologies, including CI/CD, application resiliency, and security.
In-depth knowledge of the financial services industry and its IT systems.
Practical cloud-native experience.

Nice To Haves

Familiarity with Databricks for scalable data engineering and ML platform integration.
Experience 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 such as Docker, Kubernetes, or Airflow.
Familiarity with feature stores, model registries, and ML metadata management.
Experience with infrastructure-as-code tools such as Terraform or CloudFormation.
Experience with RESTful APIs and microservices architectures.

Responsibilities

Designs, develops, codes, and troubleshoots software solutions, considering upstream and downstream systems and technical implications.
Develops secure, high-quality production code and reviews and debugs code written by others.
Leads team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes.
Establishes consistent validation standards for AI-assisted work, including secure coding, peer review, and automated testing, and promotes reuse of effective patterns.
Uses software development lifecycle tools, including authorized AI-assisted development and automation capabilities, to improve the value of automation.
Identifies opportunities to eliminate or automate remediation of recurring issues and improve application and system stability.
Leads evaluation sessions with external vendors, startups, and internal teams to assess architectural designs, technical credentials, and applicability to existing systems and information architecture.
Applies technical troubleshooting to break down solutions and solve technical problems of basic complexity.
Gathers, analyzes, and draws conclusions from large, diverse data sets to identify problems and inform secure, stable application development.
Learns and applies system processes, methodologies, and skills for developing secure, stable code and systems.
Coaches engineers on safe and compliant adoption of AI-assisted tools in delivery practices.