Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A global professional services organization is expanding its AI & Engineering practice to help enterprises transform technology platforms, modernize operations, and accelerate digital innovation. The team delivers enterprise-grade solutions across artificial intelligence, cloud infrastructure, software engineering, data platforms, and hybrid cloud environments. By leveraging advanced engineering capabilities and Generative AI technologies, the organization enables clients to modernize mission-critical systems, improve operational efficiency, and drive long-term business growth through tailored delivery models.
Key Responsibilities
- Architect and deliver enterprise AI platforms and applications on Google Cloud using Vertex AI and Gemini.
- Design, fine-tune, evaluate, deploy, and monitor Large Language Model (LLM) solutions for production environments.
- Develop Retrieval-Augmented Generation (RAG) and agentic AI solutions using Vertex AI Vector Search and BigQuery Vector Search.
- Define end-to-end AI solution architectures covering data pipelines, feature engineering, APIs, microservices, model lifecycle management, and CI/CD, MLOps, and LLMOps.
- Lead cloud-native application development utilizing Google Kubernetes Engine (GKE), Cloud Run, Pub/Sub, BigQuery, Cloud SQL, Cloud Spanner, Memorystore, and Terraform.
- Implement security, governance, and compliance best practices for AI and machine learning systems, including data privacy, adversarial protection, and enterprise guardrails.
- Collaborate with enterprise architects to align AI solutions with organizational technology strategies and governance standards.
- Design scalable AI applications using cloud-native architecture principles and modern engineering practices.
- Apply application and agentic design patterns to build resilient, maintainable, and high-performing software solutions.
- Leverage Generative AI technologies to accelerate software development and enterprise modernization initiatives.
Required Qualifications
- Bachelor’s degree in Computer Science, Engineering, or a related technical field.
- 6+ years of experience as a Software Architect or Solution Architect with expertise in enterprise application development and production-scale systems.
- 5+ years of hands-on experience with Google Cloud Platform, including at least two enterprise production implementations.
- 4+ years of experience designing Google Cloud networking, security controls, and landing zones using Terraform.
- 2+ years of experience building and operating containerized workloads on Google Kubernetes Engine (GKE).
- 2+ years of experience implementing CI/CD and DevSecOps using Cloud Build, GitHub Actions, Jenkins, or similar platforms.
- 3+ years of experience leading cloud migration or application modernization initiatives on Google Cloud.
- 2+ years of experience implementing AI and Generative AI solutions using Vertex AI and Gemini, including production deployments involving RAG, Vector Search, prompt engineering, safety policies, and observability.
- Strong knowledge of AI/ML concepts, Large Language Models (LLMs), and enterprise AI solution architecture.
- Experience delivering multiple AI solutions within enterprise production environments.
- Strong understanding of AI security, governance, data privacy, model poisoning, and adversarial attack mitigation.
- Experience working with cloud-native technologies and hyperscaler services.
- Professional Hyperscaler Architect certification (AWS, Azure, or Google Cloud Professional Cloud Architect).
- Ability to travel up to 50% based on client and project requirements.
- Limited immigration sponsorship may be available.
Preferred Qualifications
- Google Professional Machine Learning Engineer certification or an equivalent machine learning certification.
- Master’s degree in Computer Science, Engineering, Artificial Intelligence, or a related field.
- 2+ years of experience leading high-performing engineering teams delivering AI platforms or enterprise AI applications.
- 1+ year of experience implementing LLMOps or MLOps using Vertex AI Pipelines, Cloud Build, or comparable platforms.
Application Deadline
- Recruiting for this position closes on October 31, 2026.
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