Software Engineer III - ML Pipelines(Python, Pyspark , AWS)

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
- 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
- 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.
- 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
- 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
- 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
- Experience with Big Data processing frameworks (Spark preferred)
- 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