Sr. D&T Machine Learning Engineer

General Mills

5 - 12 years

50 - 55 Lacs

mumbai

Posted:2 days ago| Platform:

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Skills Required

supply chain automation sap coding analytical market intelligence workflow analytics monitoring sql

Work Mode

Work from Office

Job Type

Full Time

Job Description

Establish and Implement MLOps practices:

Development of end-to-end MLOps framework and Machine Learning Pipeline using GCP, Vertex AI and Software tools
Management of data pipelines including config, ingestion and transformation from multiple data source like Big Query, Dbt & Google cloud storage etc
Meta Data and statistics Data pipeline setup using GCP Bucket and MLMD
Re-Training and Monitoring Pipeline setup with multiple criteria Vertex AI
Serving Pipeline with multiple creation Vertex AI and GCP services
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
ML Pipeline orchestration and configuration using
DAG and Workflow orchestration using airflow/cloud
Code refactorization & coding best practices implementation as per industry standard
Technology-Stack suggestion based on 360 Deg
Implementing MLOps practices on project and follow the set MLOps
Support the ML models throughout the E2E MLOps lifecycle from development to maintenance

Architecture:

Micro Services Architecture and framework Development concept
Agile software Development concept
Architecture Design for HLD, LLD and Solution design

Team Mentoring:

Programming language Pattern Design implementation
Review projects PR and PBIs and suggestion for improvement
Knowledge sharing session with team for specific ML Ops
Guide/Mentor team members for MLOps framework development

Research, Evolve and Publish best practices:

Research and operationalize technology and processes necessary to scale ML Ops
Ability to research and recommend MLOps best practices on new technologies, platforms, and
MLOps pipeline improvement plan and suggestion

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