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)
