About Us: Waltcorp is at the forefront of cloud engineering, helping businesses transform their operations by leveraging the power of Google Cloud Platform (GCP) . We are seeking a skilled and visionary GCP DevOps Solutions Architect – ML/AI Focus to design and implement cloud solutions that address our clients' complex business challenges. Key Responsibilities: Solution Design: Collaborate with stakeholders to understand business requirements and design scalable, secure, and high-performing GCP cloud architectures . Technical Leadership: Serve as a technical advisor, guiding teams on GCP best practices, services, and tools to optimize performance, security, and cost efficiency. Infrastructure Development: Architect and oversee the deployment of cloud solutions using GCP services such as Compute Engine, Cloud Storage, Cloud Functions, Cloud SQL , and more. Infrastructure as Code (IaC) & Cloud Automation: Design, implement, and manage infrastructure using Terraform, Google Cloud Deployment Manager , or Pulumi . Automate provisioning of compute, storage, and networking resources using GCP services like Compute Engine, Cloud Storage, VPC, IAM, GKE (Google Kubernetes Engine), Cloud Run . Implement and maintain CI/CD pipelines (using Cloud Build, Jenkins, GitHub Actions , or GitLab CI ). ML Model Deployment & Automation (MLOps): Build and optimize end-to-end ML pipelines using Vertex AI Pipelines, Kubeflow , or MLflow . Automate training, testing, validation, and deployment of ML models in staging and production environments. Support model versioning, reproducibility, and lineage tracking using tools like DVC, Vertex AI Model Registry , or MLflow . Monitoring & Logging: Implement monitoring for both infrastructure and ML workflows using Cloud Monitoring, Prometheus, Grafana, Vertex AI Model Monitoring . Set up alerting for anomalies in ML model performance (data drift, concept drift). Ensure application logs, model outputs, and system metrics are centralized and accessible. Containerization & Orchestration: Containerize ML workloads using Docker and orchestrate using GKE or Cloud Run . Optimize resource usage through autoscaling and right-sizing of ML workloads in containers. Data & Experiment Management: Integrate with data versioning tools (e.g., DVC or LakeFS ) to track datasets used in model training. Enable experiment tracking using MLflow, Weights & Biases , or Vertex AI Experiments . Support reproducible research and automated experimentation pipelines. Client Engagement: Communicate complex technical solutions to non-technical stakeholders and deliver high-level architectural designs, presentations, and proposals. Integration and Migration: Plan and execute cloud migration strategies, integrating existing on-premises systems with GCP infrastructure . Security and Compliance: Implement robust security measures, including IAM policies, encryption, and monitoring , to ensure compliance with industry standards and regulations. Documentation: Develop and maintain detailed technical documentation for architecture designs, deployment processes, and configurations. Continuous Improvement: Stay current with GCP advancements and emerging trends , recommending updates to architecture strategies and tools. Qualifications: Educational Background: Bachelor’s degree in Computer Science, Information Technology, or a related field (or equivalent experience). Experience: 3+ years of experience in cloud architecture, with a focus on GCP . Technical Expertise: Strong knowledge of GCP core services , including compute, storage, networking, and database solutions. Proficiency in Infrastructure as Code (IaC) tools like Terraform , Deployment Manager , or Pulumi . Experience with containerization and orchestration tools (e.g., Docker , Kubernetes , GKE , or Cloud Run ). Understanding of DevOps practices, CI/CD pipelines, and automation . Strong command of networking concepts such as VPCs, load balancing , and firewall rules . Familiarity with scripting languages like Python or Bash . Preferred Qualifications: Google Cloud Certified – Professional Cloud Architect or Professional DevOps Engineer . Expertise in engineering and maintaining MLOps and AI applications . Experience in hybrid cloud or multi-cloud environments . Familiarity with monitoring and logging tools such as Cloud Monitoring, ELK Stack , or Datadog . [CLOUD-GCDEPS-J25] Show more Show less