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 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 Skill proficiency expectations Expert level Intermediate Level Basic Level ML Ops E2E framework Big Query/SQL Python / R Vertex AI and GCP Services Docker-Container Kubeflow/Kubernetes TFX Airflow MLflow GitHub Strong communication skills Machine Learning and Deep Learning algorithms Agile techniques Demonstrates teamworking skills. Mentor others and lead best practices. Micro Services concept Power BI, Tableau, Looker Good to have domain knowledge: Consumer Packed Goods industry and data sources Analytic toolset- dbt, atscale, neo4j, Atlassian

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