Engineering Foundry Manager

About This Role

Lead the design, architecture, and delivery of enterprise AI platforms and applications on Google Cloud, optimizing for scalability, reliability, security, and cost while mentoring engineering teams.

Responsibilities

• Architect and deliver enterprise AI platforms and applications on Google Cloud using Vertex AI and Gemini; optimize for scalability, reliability, security, and cost
• Design, fine-tune, evaluate, and govern LLM solutions with Gemini on Vertex AI
• Build RAG and agentic solutions using Vertex AI Vector Search and BigQuery vector; implement context management, retrieval strategies, and observability
• Define end-to-end architectures across data pipelines, feature engineering, model lifecycle, APIs/microservices, and CI/CD/MLOps/LLMOps with Vertex AI Pipelines and Cloud Build
• Lead cloud-native development on GKE, Cloud Run, Pub/Sub, BigQuery, Cloud SQL/Spanner, Memorystore, and Terraform; enforce application and agentic design patterns
• Implement security and governance for AI/ML systems (data privacy, model poisoning, adversarial attacks); apply Gemini safety features and enterprise guardrails
• Lead the design and development of enterprise-grade AI applications and platforms for production scaling
• Integrate and fine-tune Large Language Models (LLMs) and other AI/ML models into enterprise applications
• Collaborate with enterprise architects to align AI solutions with broader company technical strategy, governance, and standards
• Design and deploy applications using Cloud Native principles on a hyperscaler platform (AWS, Azure, GCP)

Requirements

• Bachelor's degree in Computer Science, Engineering or a related technical field
• 8+ years' experience as a Software or Solution Architect, with a strong focus on application development and scaling solutions for production environments
• 5+ years hands-on with Google Cloud, including 2+ end-to-end enterprise implementations in production
• 4+ years designing and implementing Google Cloud networks, security controls, and landing zones using Terraform
• 3+ years building and operating containerized workloads on GKE (autoscaling, ingress, monitoring/observability)
• 3+ years implementing CI/CD and DevSecOps with Cloud Build, GitHub Actions, or Jenkins
• 3+ years executing migration or modernization programs to Google Cloud (rehost, replatform, refactor)
• 2+ years applying AI/GenAI on Google Cloud with Vertex AI and Gemini, including 1+ years' production deployment
• Deep understanding of AI/ML concepts, including experience with LLMs and their application in enterprise settings
• Experience implementing multiple AI solutions in a professional, real-world environment
• Strong understanding of security implications related to AI/ML systems
• Familiarity with various hyperscaler tools and services
• Ability to travel up to 50%

Qualifications

• Bachelor's degree in Computer Science, Engineering or a related technical field
• 8+ years' experience as a Software or Solution Architect, with a strong focus on application development and scaling solutions for production environments

Nice to Have

• Google Professional Machine Learning Engineer certification or the equivalent ML certification
• Master's degree in technology-related discipline
• 2+ years leading high performance, results driven engineering teams delivering AI platforms or applications
• 1+ year implementing LLMOps/MLOps using Vertxe AI Pipelines and Cloud Build (or similar)

Skills

* Required skills

Certifications