Software Engineer III - ML Model Delivery

JPMorgan Chase & Co.; Plano (TX); On-site; USD 110,000 - 160,000; Full time

Job summary

JPMorgan Chase & Co. seeks a Software Engineer III to join the Consumer and Community Banking - Risk Technology Portfolio.

You will own technical deliverables, influence design, and work across cloud, data, and ML domains to solve business problems. Responsibilities include building ML model lifecycle components, deploying on AWS (Databricks, EMR, ECS, S3), and advancing SDLC/MLOps automation with AI-assisted tooling.

Qualifications

• 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 with demonstrated validation of outputs.
• Understanding of responsible AI use in engineering workflows and secure data handling.
• Proficiency in Python and ML libraries (Pandas, NumPy, Scikit-learn, etc.).
• Working knowledge of AWS and cloud-native development patterns.
• Practical experience with infrastructure-as-code (Terraform) and deployment automation.
• Ability to work independently on platform problems with moderate oversight.

Responsibilities

• Design, build, and maintain platform components for end-to-end ML model lifecycle from development to deployment and monitoring.
• Apply SDLC tools and AI-assisted development to improve automation and value.
• Leverage AI coding assist tools to improve code quality, delivery speed, and productivity with peer review and secure coding standards.
• Develop and maintain data and feature pipelines feeding ML models in production.
• Build and manage cloud-based infrastructure on AWS (Databricks, EMR, ECS, S3) to support 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; address performance and stability issues.
• Collaborate with data scientists to understand requirements and translate them into platform capabilities.
• Contribute to a team culture of diversity, inclusion, and continuous improvement.

Skills

Python

ML pipelines

Independent work

AI-assisted development

Education

Formal training in software engineering

Tools

Databricks

AWS

EMR

ECS

S3

Terraform

Docker

Kubernetes

Job description

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)