Designs, implements, and manages enterprise cloud infrastructure and services, primarily within Google Cloud Platform (GCP). Ensures cloud and supporting Windows infrastructure is reliable, secure, scalable, cost-effective, and aligned with architecture and operational standards.
Responsibilities
Designs, deploys, manages, and optimizes GCP infrastructure and core services, including Compute Engine, Cloud Storage, VPC networks, load balancing, Cloud CDN, and related platform components, to meet availability, scalability, performance, and cost requirements.
Designs and supports Google Kubernetes Engine (GKE) environments and collaborates with development teams to integrate, deploy, and operate applications using reliable container and cloud architecture practices.
Leads or contributes to cloud and infrastructure projects, manages assigned milestones and stakeholder communications, maintains technical and operational documentation, and provides technical guidance and support to IT team members.
Investigates and resolves complex cloud, server, networking, and platform incidents; evaluates possible causes and recovery options, coordinates escalation when needed, and supports backup, disaster recovery, and business continuity capabilities.
Designs, administers, and troubleshoots Windows Server and Active Directory services that support cloud and enterprise infrastructure, including integration, authentication, availability, and operational support requirements.
Join the Enterprise Solution Engineering Team, AIXON, to design, build, and optimize scalable, high-performance ML infrastructure that powers transformative AI solutions by integrating omnichannel customer data at scale.
Responsibilities
Architect and operate resilient ML job execution frameworks covering training, inference, and post-processing workflows
Develop and maintain API services and developer tooling to orchestrate ML workflows on Kubernetes using Argo Workflows, Helm, Terraform
Build scalable, efficient batch pipelines with Apache Spark to support large-scale ML training and evaluation
Design and maintain robust data infrastructures using Trino, Databricks and other modern database technologies, monitored with Prometheus and Grafana for high availability and observability
Develop tooling that streamlines ML experimentation, accelerates production workflows, and empowers cross-functional teams to innovate rapidly
Requirements
Bachelor’s degree in Computer Science, Engineering, or a related technical field
Design, build, and scale Google Cloud Platform-enabled data ecosystems to support enterprise decision-making and analytics.
Compensation
The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $116,200 to $229,100.
Responsibilities
Design end-to-end cloud analytics architectures using Google Cloud Platform services
Support full lifecycle implementations across data ingestion, transformation, governance, and analytics enablement
Develop and optimize cloud-based data solutions using structured and unstructured data sources
Use automation and analytics techniques to improve insight generation and operational efficiency
Lead portions of complex technology engagements and coordinate delivery across teams in multiple locations
Requirements
Bachelor’s degree or equivalent professional experience
Nice to have: Advanced degree in a technical or analytical field
Nice to have: Google Cloud Platform certifications
