Job Description
Are you ready to be at the forefront of technology, driving innovation and modernizing mission-critical systems? Join the Aumni team at JPMorgan Chase as a Lead MLOps Engineer, where your expertise will shape the future of AI/ML infrastructure. We offer unparalleled opportunities for career growth and a collaborative environment where your skills will make a significant impact.
As a Lead MLOps Engineer at JPMorgan Chase within the Aumni, you will help solve complex business problems with practical solutions. You will configure, maintain, and monitor systems and models produced by our data science teams, contributing to end-to-end operations, availability, reliability, and scalability in the AI/ML space. You will also serve as a leader and mentor to junior engineers, enabling the Data Science and ML Engineering teams to execute on our product roadmap.
Job Responsibilities
- Develop and maintain infrastructure as code to support Data Science and Machine Learning initiatives.
- Design and implement automated continuous integration and continuous delivery pipelines for AI/ML model development and training.
- Mentor junior MLOps engineers and Data Scientists, setting standards for model deployment and maintenance.
- Lead technical discussions with developers, key stakeholders, and team members to resolve complex technical problems.
- Build technical roadmaps in collaboration with senior leadership and identify risks or design optimizations.
- Proactively resolve issues before they impact stakeholders of deployed models.
- Champion the adoption of MLOps best practices within your team.
- Optimize workloads for production and manage performance and observability.
Required Qualifications, Capabilities, and Skills
- Formal training or certification in MLOps concepts and 5+ years applied experience
- Excellent communication skills for explaining technical concepts to non-technical audiences.
- Practical knowledge of MLOps culture and principles; ability to scale these ideas for multiple data science teams.
- Expertise in monitoring and observability in AI/ML, enforcing its implementation across an organization.
- Domain knowledge of machine learning applications and technical processes within the AWS ecosystem.
- Extensive expertise with Terraform, containers, and container orchestration, especially Kubernetes.
- Knowledge of continuous integration and delivery tools like Jenkins, GitLab, or GitHub Actions.
- Expert level in Python and Bash programming languages.
- Deep working knowledge of DevOps best practices, Linux, and networking internals.
- Ability to work with a geographically distributed team across multiple time zones.
Preferred Qualifications, Capabilities, and Skills
- Comfortable with team management, fostering collaboration, and presenting technical concepts to non-technical audiences.
- Experience with ML model training and deployment pipelines in the financial industry.
- Familiarity with observability concepts and telemetry collection using tools like Datadog, Grafana, Prometheus, and Splunk.
- Experience with ML engineering platforms such as Databricks and Sagemaker.
- Experience with Data Engineering technologies such as Snowflake and Airflow.
- Understanding of managing GPU-specific workloads
- AWS Solutions Architect certification or equivalent experience.
