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

JPMorgan Chase & Co. • Plano, TX

9h

About The Position

As a Software Engineer III at JPMorgan Chase within the Consumer and Community Banking Risk Technology Portfolio team, you will work on 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 address business needs.

The role focuses on platform components and data pipelines that support the full machine-learning model lifecycle, including development, training, deployment, and monitoring. You will build and manage AWS-based infrastructure and automate MLOps and software-delivery processes. You will also support the migration of legacy machine-learning workloads to scalable cloud-native platforms, monitor platform health, resolve performance and stability issues, and collaborate with data scientists and model developers.

The position includes leading responsible use of enterprise-authorized AI-assisted development tools and automation within the team. This involves establishing validation practices for correctness, performance, and security, and promoting secure, reusable engineering patterns. The role also contributes to a culture of diversity, inclusion, and continuous improvement.

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.
• Experience leading effective use of approved AI-assisted software development tools for tasks such as coding, code review, test acceleration, or troubleshooting, and setting expectations for validating AI 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 adherence to 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 using 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 through deployment and monitoring.
• Develop and maintain production data and feature pipelines that feed 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 zero-downtime migration of legacy ML workloads to scalable, cloud-native platforms.
• Monitor platform health and model-serving infrastructure, and identify and resolve performance and stability issues.
• Lead team adoption of enterprise-authorized AI-assisted engineering practices, including code review and refactoring assistance, test-strategy acceleration, and incident or root-cause analysis support.
• Establish consistent validation standards for AI-assisted work, including secure coding, peer review, and automated testing, and promote reuse of effective patterns across the team.
• Apply knowledge of SDLC toolchain capabilities, including authorized AI-assisted development and automation tools, 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.

Benefits

• Competitive total rewards package, including base salary; eligible roles may receive commission-based pay and/or discretionary incentive compensation in cash and/or forfeitable equity.
• Comprehensive health care coverage.
• On-site health and wellness centers.
• Retirement savings plan.
• Backup childcare.
• Tuition reimbursement.
• Mental health support.
• Financial coaching.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed