Job Description
Utilizes complex knowledge of machine learning engineering concepts and principles to apply skills in a business environment. Primarily focused on productionizing machine learning models and systems at scale. 

Essential Job Functions (5-7 years of related experience required):

• Delivers specific ML Ops engineering tasks such as moderate to complex level designing, developing, implementing, optimizing, and maintaining models, systems, and applications using existing and emerging technology platforms.
• Collaborates with cross-functional architecture teams to define and integrate frameworks and roadmaps for machine learning solutions.
• Projects are generally of moderate complexity.
• Consults on the design, development, and implementation of DevOps and ML Ops pipelines.
• May lead portions of deployment processes under guidance from people leader.
• Reviews, verifies, validates, and troubleshoots code to ensure high availability and high performance of machine learning models and applications.
• Uses complex knowledge and understanding of code management principles and best practices to follow architectural and governance guidelines.
• Effectively communicates and applies machine learning engineering value, concepts, and strategies across multiple scenarios.

Additional Qualification:

• Demonstrated analytical skills. Demonstrated problem solving skills. Possesses strong technical aptitude.
• Experience with building and maintaining end-to-end machine learning pipelines in production environments.
• Experience with model and data versioning, model deployment, model serving and monitoring. Intermediate knowledge of workflow orchestration processes and technologies.
• Ability to simultaneously handle multiple priorities.
• Seeks to acquire knowledge in area of specialty.

Special Skill Requirements (5-7 years of related experience required):

• Strong Coding experience with Python
• Object Oriented Programing
• AWS – SageMaker
• Git, GitHub

Additional Qualification:

• Demonstrated analytical skills.
• Demonstrated problem solving skills.
• Possesses strong technical aptitude.
• Experience with designing and building scalable enterprise ML Ops frameworks and pipelines including integration, testing, deployment, monitoring, infrastructure management, audit and governance.
• Advanced understanding and practical application of workflow orchestration processes and technologies.