Software Engineer III - ML Model Delivery

Our Corporate Technology team relies on smart, driven people like you to develop applications and provide tech support for all our corporate functions across our network. Your efforts will touch lives all over the financial spectrum and across all our divisions: Global Finance, Corporate Treasury, Risk Management, Human Resources, Compliance, Legal, and within the Corporate Administrative Office. You’ll be part of a team specifically built to meet and exceed our evolving technology needs, as well as our technology controls agenda.

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 Consumer and Community Banking - Risk Technology Portfolio team, you will be part of an agile team that builds and delivers trusted technology products in a secure, stable, and scalable way. You will take ownership of technical deliverables, contribute to design decisions, and work across cloud, data, and machine learning domains to solve real business problems.

Job Responsibilities:

• Design, build, and maintain platform components that support end-to-end ML model lifecycle — from development and training to deployment and monitoring
• 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
• Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards.
• Develop and maintain data and feature pipelines that feed ML models in production
• Build and manage cloud-based infrastructure on AWS (including Databricks, EMR, ECS, and S3) to support model training and serving workloads
• Automate model deployment, testing, and release processes within the SDLC/MLOps toolchain
• Support migration of legacy ML workloads to cloud-native, scalable platforms with zero downtime
• Monitor platform health and model serving infrastructure; identify and resolve performance and stability issues
• Apply AI-assisted development tools and best practices to improve code quality and delivery speed
• Collaborate with data scientists and model developers to understand requirements and translate them into reliable platform capabilities
• Contribute to a team culture of diversity, inclusion, and continuous improvement

Required Qualifications, Capabilities, and Skills:

• Formal training or certification in software engineering and 3+ years of applied experience
• Hands-on experience building and maintaining production data or ML pipelines
• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate AI-generated outputs.
• Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations.
• Proficiency in Python and experience with ML libraries and frameworks (Pandas, NumPy, Scikit-learn, etc.)
• Working knowledge of cloud platforms, particularly AWS, and cloud-native development patterns
• Practical experience with infrastructure-as-code and deployment automation (Terraform preferred)
• Ability to work independently on platform problems with moderate oversight

Preferred Qualifications, Capabilities, and Skills:

• Experience with Databricks for model training and data pipeline development
• Familiarity with MLOps practices — model versioning, experiment tracking, feature stores, and model monitoring
  AWS certifications (e.g., Solutions Architect Associate)
• Exposure to RAG architectures or GenAI/LLM integration patterns
• Knowledge of container-based deployment (Docker, ECS, or Kubernetes)
• Interest in AI-assisted engineering tools and automation within the SDLC
• Experience with Big Data processing frameworks (Spark preferred)