Lead Software Engineer - MLOps

As a Lead MLOps Engineer at JPMorgan Chase within the Aumni team, you will play a crucial role in shaping the future of AI/ML infrastructure. You will be at the forefront of technology, driving innovation, and modernizing mission-critical systems with your expertise. Your contributions will have a significant impact, and you will have unparalleled opportunities for career growth in a collaborative environment. **Key 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. **Qualifications Required:** - Formal training or certification in MLOps concepts and 5+ years of 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.,