Sr. Machine Learning Engineer

Key Skills

Aartificial intelligence, Algorithms, Apache Airflow, Data science techniques, Data Transformation, Deep Learning, Effective communication skills, Google BigQuery, Google Cloud Platform (GCP), Kubeflow, Kubernetes-K8s, Machine learning techniques, SQL

Job Description

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.

Role Responsibilities

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 service.
• Automated pipeline Development for Continuous Integration (CI)/Continuous Deployment (CD) Continuous Monitoring (CM)/Continuous Training (CT) using GCP-native tool stack.
• Branching strategies and Version Control using GitHub
• ML Pipeline orchestration and configuration using Kubeflow.
• DAG and Workflow orchestration using airflow/cloud composer.
• Code refactorization & coding best practices implementation as per industry standard
• Technology-Stack suggestion based on 360 Deg Analysis.
• Implementing MLOps practices on project and follow the set MLOps practices.
• 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 topics.
• 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 services.
• MLOps pipeline improvement plan and suggestion

Communication and Collaboration:

• Collaborate with technical teams like Data Science Lead, Data Scientist, Data Engineer and Platform owner.
• 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 meetup

Documentation:

• Document MLOps Process, Development, Architecture & Innovation etc and be instrumental in reviewing the same for other team members.

Must - have technical skills and experience

• Minimum qualification- Bachelor’s degree (Full Time)
• Total Experience required 12-15 Years
• Expertise and at least 5 Years of professional experience in MLOps E2E framework
• Expertise in Data Transformation and Manipulation through Big-Query/SQL
• Professional experience Vertex AI and GCP Services
• Expertise in one of the programming Language Python/R
• Airflow/Cloud composer Experience
• Kubernetes/Kubeflow Experience
• MLflow Professional experience
• TFX Professional experience
• Docker -container Experience
• At least 5yrs of professional experience in the related field of Data Science
• 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.

Good to have skills

• GCP certification
• Understanding of CPG industry
• Basic understanding of dbt.
• AutoML Concept
• Machine Learning -Concept of Algorithms
• Deep Learning- Concept of Algorithms
• Time Series Analysis- Concept of Algorithms