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Flex
United States · remote · USD 197900-272100 annual
Flex is seeking a Platform Architect to design and scale enterprise-grade platforms for deploying AI and data-driven applications across our global manufacturing and supply chain ecosystem. This role will focus on building secure, scalable, and developer-friendly platforms that accelerate the adoption of AI solutions—leveraging Google Cloud as a primary environment while enabling interoperability across Azure and AWS—and aligning with enterprise governance, compliance, and operational excellence standards.
You will operate at the intersection of AI platform engineering, cloud architecture, and developer experience, enabling teams across engineering, data science, and business functions to rapidly build and deploy production-grade AI solutions.
Architect and evolve enterprise AI platforms supporting LLMs, AI agents, and advanced analytics workloads
Design scalable deployment patterns for AI solutions (APIs, agents, pipelines) across cloud environments
Enable self-service AI capabilities through reusable platform services and standardized deployment frameworks
Lead architecture and implementation of AI and data platforms primarily on Google Cloud (Vertex AI, BigQuery, Cloud Run, Composer, etc.)
Define and implement multi-cloud design patterns spanning Google Cloud, Azure, and AWS, ensuring portability and flexibility
Establish consistent infrastructure, networking, and governance models across cloud providers
Build and evolve internal developer platforms that simplify deployment of AI and data applications
Apply on source siteListed via Himalayas
Develop CI/CD frameworks that support consistent deployment across multi-cloud environments
Improve observability, reliability, and developer productivity through platform tooling and automation
Design architectures integrating AI systems with enterprise data sources (ERP, supply chain, manufacturing systems) across cloud environments
Enable real-time and batch data pipelines using cloud-native and cross-cloud data services
Support scalable, governed data platforms leveraging modern architectures (lakehouse, medallion, etc.)
Implement secure-by-design architectures across Google Cloud, Azure, and AWS, including IAM, network controls, and data protection
Establish guardrails for AI deployment, including access control, monitoring, and auditability
Ensure compliance with global regulatory standards (GDPR, FDA, and other industry requirements)
Partner with Data Science, R&D, IT, and business stakeholders to translate use cases into scalable platform capabilities
Support lighthouse AI initiatives and drive broader enterprise adoption
Mentor engineers and promote best practices in platform engineering and cloud architecture
10+ years of experience in platform engineering, cloud architecture, or related roles
Strong experience designing and deploying solutions on Google Cloud Platform
Experience building and scaling cloud-native platforms (APIs, microservices, data pipelines)
Hands-on experience with infrastructure-as-code (Terraform or similar) and CI/CD practices
Experience supporting production-grade data and analytics platforms
Proficiency in Python, Java, Go, or similar
Experience working in multi-cloud environments (GCP, Azure, AWS)
Experience with AI/ML platforms (Vertex AI, SageMaker, Azure ML, or similar)
Familiarity with modern data architectures (BigQuery, Snowflake, lakehouse, etc.)
Experience building internal developer platforms or self-service tooling
Exposure to enterprise security frameworks (IAM, zero trust, network segmentation)
Experience in manufacturing, supply chain, or industrial environments
What you'll receive for the great work you provide:
Applications for this job position will be accepted for at least five days following the job posting start date below and continuing until the end date below or until the position is filled. This posting may close sooner due to application volume.
The base pay range for this position is provided below. The final base rate offered will be determined using job-related, non-discriminatory criteria, including but not limited to experience, qualifications, geographic location, education, external market data, and internal equity.
Originally posted on Himalayas