If you are a seasoned MLOps Engineer with a passion for leading teams and driving technical innovation in AWS, Python, machine learning, React, CI/CD, and in-house API development, we invite you to join us in advancing our mission and making a significant industry impact.
We are seeking a Senior MLOps Engineer with deep expertise in machine learning operations, AWS, Python, ReactJS, CI/CD, and API development for MLOps tools. In this senior role, you will provide technical leadership and guidance to the MLOps team, overseeing the deployment, monitoring, and optimization of machine learning models and systems. You will also drive the enhancement of in-house tools for improved automation and operational efficiency, while collaborating with cross-functional teams to scale MLOps processes across the organization.
Key Responsibilities:
1. Machine Learning Operations:
- Lead and mentor MLOps engineers in deploying machine learning models into production.
- Architect, develop, and maintain scalable machine learning pipelines for deployment, monitoring, and evaluation.
- Collaborate with data scientists and machine learning engineers to refine models and optimize operational workflows.
2. AWS Infrastructure Leadership:
- Strategically manage and optimize cloud infrastructure on AWS, ensuring performance and cost-efficiency for machine learning workloads.
- Oversee the implementation of Infrastructure as Code (IaC) using tools such as AWS CloudFormation, ensuring scalability and automation.
- Leverage AWS services for advanced storage, compute, and data processing solutions.
3. Python, API, and Web Development:
- Lead the design and development of robust, secure APIs and automation tools using Python to support MLOps processes.
- Guide the team in building scalable and well-documented in-house APIs for MLOps tools.
- Drive collaboration with internal teams to align API development with evolving requirements and business goals.
- Oversee the development of web applications using React and modern technologies to enhance MLOps tooling.
4. CI/CD Implementation and Optimization:
- Lead the design, implementation, and optimization of CI/CD pipelines for machine learning models and application deployment.
- Drive automation of testing, deployment, and rollback processes, ensuring high reliability and efficiency.
- Continuously refine and enhance CI/CD workflows to support the evolving needs of the machine learning infrastructure.