Senior MLOps Engineer
Job Description
Responsibilities
Lead engineering activities to build production-grade Pricing and Promotion science models and play a pivotal role in implementing engineering operations to ensure seamless deployment, monitoring, and management of machine learning models and data pipelines.
Collaborate closely with data scientists, data engineers, and platform engineers to establish CI/CD and infrastructure-as-code frameworks, develop reusable pipelining libraries, design scalable, highly fault-tolerant architectures, implement model observation and performance monitoring, and deploy/support enterprise-grade ML solutions.
Create and manage documentation and knowledge bases, including development best practices, MLOps procedures, and CI/CD processes and procedures.
Implement best practices for version control, testing, and deployment.
Skills
Hands-on with DevOps, MLOps (ML Operations), and container-based application development (Docker, Kubernetes, etc.)
Expertise designing and implementing data pipelines using modern data engineering approach and tools: AWS, PySpark, Python, Docker, etc.
Understanding of data science analytical tools (e.g. Hadoop, Spark, AWS, Sagemaker, Jupyter Notebooks, etc.)
Proven ability to take complex business analyses and projects from concept to completion and make the results accessible and relevant to business users at all levels.
Self-directed, innovative thinker with a strong attention to detail and commitment to consistently meeting timelines and operating from a sense of urgency.
Comfortable with uncertainty: projects and assignments will change rapidly so must be flexible enough to accommodate changing priorities and timelines.
Ability to effectively present information and respond to questions in one-on-one and large group situations involving senior leadership and other teams in the organization.
Education & Experience
5+ years of experience as an MLOps engineer, or 5+ years of experience as a DevOps engineer with 2+ years of experience building, training, and deploying ML models.
Bachelor’s degree in computer science, Engineering, or a related field.
Hands-on with AWS and Python.