About Company:

We make food?the world loves: 100 brands. In 100 countries. Across six continents. With iconic brands like Cheerios, Pillsbury, Betty Crocker, Nature Valley, and Häagen-Dazs, we’ve been serving up food the world loves for 155 years (and counting). Each of our brands has a unique story to tell.


How we make our food is as important as the food we make. Our values are baked into our legacy and continue to accelerate us into the future as an innovative force for good. Our Company was founded in 1866 when Cadwallader Washburn boldly bought the largest flour mill west of the Mississippi. That pioneering spirit lives on today through our leadership team who upholds a vision of relentless innovation while being a force for good.

Role Responsibilities:

Model and Data Pipelines:

• Develop automated processes for large scale data pipelines, model development, operationalization and model monitoring.
• Develop and automate the MLOps pipeline.
• Deployment on low code env and developing integrations
• Take responsibility for production issues, perform root cause analysis, and recommend changes to reduce/eliminate re-occurrence of issues.
• Accurately apply technical job knowledge and skills to complete all work in a timely manner in accordance with policies, procedures and regulatory requirements

Automate and Improve Efficiency:

• Automate monitoring of models both for accuracy degradation and failures
• Optimize deployment and change control processes for models
• Automate logging of model usage and predictions provided

Research, Evolve and publish best practices:

• Recommend model refinements to optimize cloud spend
• Enrich existing ML frameworks and libraries
• Research and operationalize technology and processes necessary to scale ML models
• Ability to research and recommend best practices on new technologies, platforms and services
• Improve ML pipeline documentation and understandability

Communication and Collaboration:

• Collaborate with technical teams like data scientist, data developers, development and platform
• Knowledge sharing with the broader analytics team and stakeholders is essential
• Communicate on the on-goings to embrace the remote and cross geography culture
• Align on the key priorities and focus areas
• Ability to communicate the accomplishments, failures and risks in timely manner

Embrace learning mindset:
• Continually invest in your own knowledge and skillset through formal training, reading, and attending conferences and meetups

Must - have technical skills and experience:
• Expertise and at least 3yrs of professional experience in MLOps
• Software engineering skills and expertise in SQL
• ML skills- DL, neural network architectures, NLP, graph
• Knowledge of ML and AI frameworks
• Knowledge of GCP Vertex AI
• At least 5yrs of professional experience in the related field of data science or MLOps
• Professional experience with a major cloud computing platform such as GCP or Azure
• Understanding of AutoML and ensembled models and its pipelines
• Strong communication skills both verbal and written including the ability to interact effectively with colleagues of varying technical and non-technical abilities.
• Passionate about agile software processes, data-driven development, reliability, and systematic experimentation.
• Passion for learning new technologies and solving challenging problems.

Good to have skills:

• GCP certification
• Understanding of CPG industry
• Basic understanding of dbt