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