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

JPMorganChase•Plano, TX

About The Position

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 building and delivering trusted technology products in a secure, stable, and scalable way. You will own technical deliverables, contribute to design decisions, and work across cloud, data, and machine learning domains to solve business problems. The role focuses on platform components and pipelines supporting the end-to-end machine learning model lifecycle, from development and training through deployment and monitoring. It also involves building and managing AWS infrastructure, automating MLOps and SDLC processes, migrating legacy workloads to cloud-native platforms, and maintaining platform health. You will collaborate with data scientists and model developers and help the team adopt approved AI-assisted engineering practices with appropriate validation, security, and testing standards.

Requirements

Formal training or certification in software engineering and at least 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, such as for coding, code review, test acceleration, or troubleshooting, and setting expectations for validating outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, and compliance with resiliency and security expectations; experience coaching engineers on safe, compliant adoption.
Proficiency in Python and experience with ML libraries and frameworks such as Pandas, NumPy, and Scikit-learn.
Working knowledge of cloud platforms, particularly AWS, and cloud-native development patterns.
Experience with big data processing frameworks; Spark is preferred.
Practical experience with infrastructure as code and deployment automation.
Ability to work independently on platform problems with moderate oversight.

Nice To Haves

Experience with Databricks for model training and data pipeline development.
Familiarity with MLOps practices, including model versioning, experiment tracking, feature stores, and model monitoring.
AWS certifications, such as Solutions Architect Associate.
Exposure to RAG architectures or GenAI/LLM integration patterns.
Knowledge of container-based deployment, such as Docker, ECS, or Kubernetes.
Interest in AI-assisted engineering tools and automation within the SDLC.
Terraform experience.

Responsibilities

Design, build, and maintain platform components supporting the end-to-end ML model lifecycle, from development and training to deployment and monitoring.
Develop and maintain data and feature pipelines that feed production ML models.
Build and manage AWS cloud infrastructure, including Databricks, EMR, ECS, and S3, for model training and serving workloads.
Automate model deployment, testing, and release processes within the SDLC/MLOps toolchain.
Support migration of legacy ML workloads to scalable, cloud-native platforms with zero downtime.
Monitor platform health and model-serving infrastructure, and identify and resolve performance and stability issues.
Promote effective use of enterprise-authorized AI-assisted engineering tools and practices to improve code quality, delivery speed, and operational outcomes.
Establish consistent validation standards for AI-assisted work, including secure coding, peer review, and automated testing, and promote reuse of effective team patterns.
Use SDLC toolchain capabilities, including authorized AI-assisted development and automation, 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.