Job Title

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]