Software Engineer III - ML Python
Software Engineer III - ML Python
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Job Description
We have an opportunity to impact your career and provide an adventure where you can push the limits of whats possible.
As a Software Engineer III at JPMorgan Chase within Corporate Sector Technology line of business youwill be an integral part of an agile team that works to enhance build and deliver trusted marketleading 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 firms business objectives.
This role involves working with cuttingedge technologies to ensure scalable reliable and efficient AI solutions. The ideal candidate will be adept at building and supporting robust ML solutions. 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 crossfunctional 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 highquality 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 crossfunctional 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 and 3 years applied experience
- Handson practical experience delivering system design application development testing and operational stability
- Strong Python Programming Skills including Pandas Numpy and ScikitLearn.
- Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g. cloud artificial intelligence machine learning)
- Practical cloud native experience
- Strong expertise in deploying and managing machine learning models in production environments
- Proficiency in building and maintaining CI/CD pipelines for machine learning workflows.
- Proficiency in cloud platforms (e.g. AWS Google Cloud Azure) containerization technologies (e.g. Docker Kubernetes)
- Familiarity with monitoring and logging tools (e.g. Prometheus Grafana ELK Stack)
Preferred qualifications capabilities and skills
- Proven experience in deploying and managing largescale machine learning models in production environments
- Working knowledge of security best practices and compliance standards for Machine Learning systems
- Experience with infrastructure optimization techniques to enhance performance and efficiency
- Development of REST APIs using frameworks such as Flask or FastAPI for seamless integration into business solutions
- Strong ability to monitor ML models in production addressing model performance and data quality issues effectively
- Familiarity with creating and utilizing synthetic datasets to improve model training and evaluation
Employment Type
Full-Time
