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 problemsDevelops secure high-quality production code, and reviews and debugs code written by othersIdentifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systemsLeads 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 applicationsDevelop and implement strategies for deploying machine learning models into production, ensuring scalability, reliability, and efficiencyDesign and maintain continuous integration and continuous deployment (CI/CD) pipelines to automate the testing, deployment, and updating of machine learning modelsManage 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 experienceHands-on practical experience delivering system design, application development, testing, and operational stabilityProficient in all aspects of the Software Development Life CycleDemonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)Proficient in Advanced Python Programming Skills including Pandas, Numpy and Scikit-Learn. Practical cloud native experienceStrong expertise in deploying and managing machine learning models in production environmentsProficiency in building and maintaining CI/CD pipelines for machine learning workflows.Expertise 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).Strong ability to monitor ML models in production, addressing model performance and data quality issues effectively

Preferred qualifications, capabilities, and skills

Proven experience in deploying and managing large-scale machine learning models in production environmentsWorking knowledge of security best practices and compliance standards for Machine Learning systemsExperience with infrastructure optimization techniques to enhance performance and efficiencyDevelopment of REST APIs using frameworks such as Flask or FastAPI for seamless integration into business solutionsFamiliarity with creating and utilizing synthetic datasets to improve model training and evaluationStrong SQL skills a plus