MLOps Engineer - Python/Tensorflow (3-5 yrs)

Desired candidate should have :

- Bachelor`s degree in Computer Science, Engineering or Technical Field preferred.

- Minimum 3-5 years of relevant experience.

- Proven experience in machine learning engineering and operations.

- Profound understanding of machine learning concepts, model lifecycle management, and experience in model management capabilities including model definitions, performance management and integration.

- Execution of model deployment, monitoring, profiling, governance and analysis initiatives.

- Excellent interpersonal, oral, and written communication; Ability to relate ideas and concepts to others; write reports, business correspondence, project plans and procedure documents.

- Solid Python, ML frameworks (e.g., TensorFlow, PyTorch), data modeling, and programming skills.

- Experience and strong understanding of cloud architecture and design (AWS, Azure, GCP).

- Experience using modern approaches to automating machine learning pipelines.

- Agile and Waterfall methodologies.

- Ability to work independently and manage multiple task assignments within a structured implementation methodology.

- Personally invested in continuous improvement and innovation.

- Motivated, self-directed individual that works well with minimal supervision.

- Must have experience working across multiple teams/technologies.

Roles and Responsibilities:

- Work with stakeholders to define machine learning solution design (based on cloud services like Azure and Snowflake), that adheres to industry best practices.

- Design and develop machine learning pipelines and frameworks to support enterprise analytical/reporting needs. Provide guidance and collaborate on model deployment design with data science, ML engineering, and data quality teams.

- Manage and configure the cloud environments and related ML services.

- Implement improvements and new tools and approaches aimed at model integration, storage, profiling, processing, management and archival.

- Recommend improvements and platform development to meet strategic objectives of Global Technology and the MPG business.

Utilize Agile practices to manage and deliver features

- Must stay current with key technologies and practices so that both current and new needs can be assessed for suitability to business requirements.

- Collaborate with the Data Science Lead, Product Architect and market-specific teams to define and support the implementation of machine learning solutions that align with business objectives.