What are the responsibilities and job description for the Applied AI ML Lead - ML Ops, CTC position at JPMorgan Chase?
Step into a fast-growing area of Cybersecurity at JPMorganChase, where you can help build and deploy machine learning solutions that directly support Cyber Operations. In this role, you’ll work independently and apply your skills in data analysis, statistics, and data engineering to deliver machine learning models that drive real business outcomes. You’ll join a global Cybersecurity team, collaborating with technologists and innovators who protect our assets every day.
As an ML Ops Engineer within Corporate Sector in Cybersecurity & Tech Controls team, you will deploy, monitor, and manage machine learning models in production environments using the latest technologies. You’ll automate model deployment, optimize infrastructure, and ensure AI systems perform reliably and efficiently. Your collaboration with cross-functional teams and your problem-solving skills will help drive innovation and deliver impactful AI solutions.
Job responsibilities
- Work closely with data scientists and software engineers to integrate machine learning models into applications.
- Deploy machine learning models into production, ensuring they are scalable, reliable, and efficient.
- Build and maintain CI/CD pipelines to automate testing, deployment, and updates for machine learning models.
- Manage and optimize infrastructure for running models, including cloud services, Docker, and Kubernetes.
- Set up monitoring and logging to track model performance, detect anomalies, and ensure smooth operation.
- Maintain version control for models and data, supporting traceability and compliance.
- Apply security best practices and ensure models meet regulatory standards.
Required qualifications, capabilities, and skills
- Bachelor’s degree in Computer Science, Engineering, or a related field, with relevant ML Ops experience.
- Experience deploying and managing machine learning models in production environments.
- Skilled in building and maintaining CI/CD pipelines for machine learning workflows.
- Proficient with cloud platforms (AWS, Google Cloud, Azure) and containerization tools (Docker, Kubernetes).
- Familiar with monitoring and logging tools (Prometheus, Grafana, ELK Stack).
- Advanced Python programming skills.
- Strong problem-solving skills and attention to detail.
- Effective communicator, able to collaborate with cross-functional teams.
- Bachelor’s degree in Computer Science, Engineering, or a related field, with relevant ML Ops experience.
Preferred qualifications, capabilities, and skills
- Experience deploying and managing large-scale machine learning models in production.
- Ability to monitor models in production and address performance and data quality issues.
- Working knowledge of security best practices and compliance standards for ML systems.
- Experience optimizing infrastructure for performance and efficiency.
- Developed REST APIs using frameworks like Flask or FastAPI.
- Familiarity with synthetic datasets for model training and evaluation.
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