Software Engineer III - ML Ops

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 Corporate Sector line of business, you will be an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way.

As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives.

We are seeking a highly skilled ML Ops Engineer with expertise in deploying, monitoring, and managing machine learning models and pipelines.

This role involves working with cutting-edge technologies to ensure scalable, reliable, and efficient AI solutions.

The ideal candidate will be adept at building robust infrastructure and processes to support the seamless operation of machine learning models.

In this role, you will be responsible for automating model deployment, optimizing infrastructure, and ensuring the continuous performance of AI systems.

Your ability to collaborate with cross-functional teams and address operational challenges will be crucial to driving innovation and delivering impactful AI solutions.

Job responsibilities


* Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems


* Develops secure high-quality production code, and reviews and debugs code written by others


* Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems


* Leads communities of practice across Software Engineering to drive awareness and promotes use of ML blueprints


* Collaborate with cross-functional teams, including data scientists and software engineers, to understand model requirements and integrate them into applications


* Develop and implement strategies for deploying machine learning models into production, ensuring scalability, reliability, and efficiency


* Design and maintain continuous integration and continuous deployment (CI/CD) pipelines to automate the testing, deployment, and updating of machine learning models


* Manage and optimize the infrastructure required for running machine learning models, including cloud services, containerization (e.g., Docker), and orchestration tools (e.g., Kubernetes)


* Implement monitoring and logging solutions to track model performance, detect anomalies, and ensure models are operating as expected in production.


* Respond to incidents and troubleshoot issues related to model performance, data quality, and infrastructure

Required qualifications, capabilities, and skills


* Formal training or certification on software engineering concepts an...