Software Engineer III, ML Model Delivery

JPMorgan Chase

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Plano, TX
Employment: Full-time
Posted: 27 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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Salary context

How this pay compares to similar roles

Similar $217k

$152k $291k

below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $179,968–$253,812.

Based on 240 similar postings.

Employer

About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1226 open roles on FindRole.

Listed pay typically runs $156,750–$215,000 across 5 roles with salary data.

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At a glance

TL;DR · Software Engineer III, ML Model Delivery

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As a Software Engineer III - ML Model Delivery, you will join the Consumer and Community Banking - Risk Technology Portfolio team to build and deliver trusted technology products. You will take ownership of technical deliverables, contributing to design decisions across cloud, data, and machine learning domains. Your daily responsibilities include designing and maintaining platform components for the end-to-end ML model lifecycle, developing data and feature pipelines, and managing cloud-based infrastructure on AWS using Databricks, EMR, ECS, and S3. You will automate model deployment and testing within the MLOps toolchain while migrating legacy workloads to cloud-native platforms. Required skills include Python, Pandas, NumPy, Scikit-learn, and Terraform. You must also utilize enterprise-authorized AI-assisted development tools to improve code quality and productivity while ensuring secure, responsible AI practices.

What does a Software Engineer earn in Texas?

Median $178175 from 187 postings across 44 companies.

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Skills

Python AWS MLOps Databricks Terraform Spark Docker Kubernetes ECS S3 EMR Pandas NumPy Scikit-learn RAG LLM infrastructure-as-code ML Pipelines

What you'll do

Design, build, and maintain platform components for the end-to-end ML model lifecycle.
Develop and maintain data and feature pipelines to feed production ML models.
Build and manage cloud-based infrastructure on AWS including Databricks, EMR, ECS, and S3.
Automate model deployment, testing, and release processes within the MLOps toolchain.
Migrate legacy ML workloads to cloud-native, scalable platforms with zero downtime.
Monitor platform health and resolve performance or stability issues in model serving infrastructure.
Use AI-assisted development tools to generate, refactor, and test code while ensuring security standards.

What we're looking for

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 to code, test, troubleshoot, and document.
Understanding of responsible AI use, including data sensitivity, secure handling of inputs/outputs, and security expectations.
Proficiency in Python and experience with ML libraries and frameworks like Pandas, NumPy, and Scikit-learn.
Working knowledge of cloud platforms, particularly AWS, and cloud-native development patterns.
Practical experience with infrastructure-as-code and deployment automation (Terraform preferred).
Experience with Databricks, MLOps practices, AWS certifications, RAG architectures, container-based deployment, and Spark (preferred).

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