Job Description:
Job Title
Sr. D&T Machine Learning Engineer
We exist to make food the world loves. But we do more than that. Our company is a place that prioritizes being a force for good, a place to expand learning, explore new perspectives and reimagine new possibilities, every day. We look for people who want to bring their best — bold thinkers with big hearts who challenge one other and grow together. Because becoming the undisputed leader in food means surrounding ourselves with people who are hungry for what’s next.
OVERVIEW
General Mills, Digital and Technology India, is seeking Sr Machine Learning Engineer to join the Enterprise Data Capabilities Organization. This team builds enterprise level scalable and sustainable data and model pipelines to serve the analytic needs of business impacting problem statements. In this role, you are a critical member of the data science team focused to operationalize the ML and AI models, entails model management and monitoring too. The success is to recommend innovative ways to automate the MLOps pipelines on GCP and set standards that would ensure repeated success.
This capability is leveraged to fuel advanced Analytical solutions, Machine Learning and Deep Learning. It is also responsible for implementing and enhancing community of practice to determine the best practices, standards, and MLOps frameworks to efficiently delivery enterprise data solutions at General Mills.
This role works in close collaboration with Data Scientists, Data Engineers, Platform Engineers and Tech Expertise to support the analytic consumption needs. Enhances the performance of the models and automates the production pipelines to gain efficiency.
KEY ACCOUNTABILITIES
Establish and Implement MLOps practices:
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Development of end-to-end MLOps framework and Machine Learning Pipeline using GCP, Vertex AI and Software tools
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Management of data pipelines including config, ingestion and transformation from multiple data source like Big Query, Dbt & Google cloud storage etc
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Meta Data and statistics Data pipeline setup using GCP Bucket and MLMD
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Re-Training and Monitoring Pipeline setup with multiple criteria Vertex AI
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Serving Pipeline with multiple creation Vertex AI and GCP services
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Resource and Infra Monitoring configuration and pipeline development using GCP
- Automated pipeline Development for Continuous Integration (CI)/Continuous Deployment (CD) Continuous Monitoring (CM)/Continuous Training (CT) using GCP-native tool
- Branching strategies and Version Control using GitHub
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ML Pipeline orchestration and configuration using
- DAG and Workflow orchestration using airflow/cloud
- Code refactorization & coding best practices implementation as per industry standard
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Technology-Stack suggestion based on 360 Deg
- Implementing MLOps practices on project and follow the set MLOps
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Support the ML models throughout the E2E MLOps lifecycle from development to maintenance
Architecture:
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Micro Services Architecture and framework Development concept
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Agile software Development concept
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Architecture Design for HLD, LLD and Solution design
Team Mentoring:
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Programming language Pattern Design implementation
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Review projects PR and PBIs and suggestion for improvement
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Knowledge sharing session with team for specific ML Ops
- Guide/Mentor team members for MLOps framework development
Research, Evolve and Publish best practices:
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Research and operationalize technology and processes necessary to scale ML Ops
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Ability to research and recommend MLOps best practices on new technologies, platforms, and
- MLOps pipeline improvement plan and suggestion
Communication and Collaboration:
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Collaborate with technical teams like Data Science Lead, Data Scientist, Data Engineer and Platform
- Knowledge sharing with the broader analytics team and stakeholders is
- Communicate on the on-goings to embrace the remote and cross geography
- Align on the key priorities and focus
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Ability to communicate the accomplishments, failures, and risks in timely manner.
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Continually invest in your own knowledge and skillset through formal training, reading, and attending conferences and meetup
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Document MLOps Process, Development, Architecture & Innovation etc and be instrumental in reviewing the same for other team members
MINIMUM QUALIFICATIONS
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Education: Mininimum qualification is Bachelor's degree (full time)
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Experience: Minimum 5yrs of professional experience in MLOps E2E framework
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Technical Skills: Expertise in Data Transformation and Manipulation through Big-Query/SQL, Professional experience with Vertex AI and GCP Services, Expertise in one of the programming Language Python/R, Should have experience in Airflow/Cloud composer, Kubernetes/Kubeflow, MLflow, TFX,Docker -container
- Soft Skills: Strong communication skills both verbal and written including the ability to interacteffectively with colleagues of varying technical and non-technical. Passionate about agile software processes, data-driven development, reliability, and systematic
PREFERRED QUALIFICATIONS
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GCP certification
- Understanding of CPG industry
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Bcsic understanding of dbt
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AutoML Concept
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Machine Learning -Concept of Algorithms
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Deep Learning- Concept of Algorithms
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Time Series Analysis- Concept of Algorithms
