GCP ML Architect -Data

Role : GCP ML Architect -Data Location : Chaska MN (100% onsite) Detailed JD: Responsible for designing, implementing, and managing data and machine learning solutions on Google Cloud Platform Key Responsibilities: Design end-to-end data solutions, including data ingestion, storage, processing, and analysis pipelines, as well as machine learning model development, deployment, and monitoring pipelines. Design and implement scalable, secure, and cost-optimized cloud infrastructure using GCP services like BigQuery, Dataflow, Dataproc, Cloud Storage, and Kubernetes Engine. Design and implement data models, ensuring data consistency, accuracy, and accessibility for various applications and users. Establish MLOps practices, enabling the automation of machine learning model training, deployment, and monitoring. Ensure that all data solutions adhere to security and compliance standards, implementing access controls, encryption, and other security measures. Monitor and optimize the performance of data and machine learning systems, ensuring they meet business requirements and SLAs. Develop and implement strategies for managing and optimizing cloud costs, ensuring efficient resource utilization. They provide technical guidance and mentorship to other team members, fostering a culture of best practices and continuous improvement. Key Skills: 10+ years of experience designing and developing production grade data architectures using google cloud data services and solutions Proficiency in BigQuery, Dataflow, Dataproc, Cloud Storage, pub-sub, Kubernetes Engine, and other relevant GCP services. Strong Experience with data warehousing, ETL processes, data modeling, and data pipeline development. Strong hands on experience in Python and SQL Strong experience of model development, deployment, and monitoring using Vertex AI Good experience of LLM, agents and agentic AI, Agent Space and hands on RAG experience Experience with cloud computing concepts, including infrastructure as code (IaC), scalability, security, and cost optimization.

Created: 2026-03-04