Engineering Foundry Manager

About This Role

Lead the design and development of enterprise-grade AI applications and platforms on Google Cloud, focusing on scalable and secure production solutions. This role involves integrating and fine-tuning Large Language Models, collaborating with enterprise architects, and ensuring security within AI/ML systems.

Responsibilities

• Architect and deliver enterprise AI platforms and applications on Google Cloud using Vertex AI and Gemini, optimizing for scalability, reliability, security, and cost
• Design, fine-tune, evaluate, and govern LLM solutions with Gemini on Vertex AI, implementing deployment, inference optimization, and monitoring
• Build RAG and agentic solutions using Vertex AI Vector Search and BigQuery vector, implementing 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, enforcing application and agentic design patterns
• Implement security and governance for AI/ML systems, applying Gemini safety features and enterprise guardrails
• Integrate and fine-tune Large Language Models (LLMs) and other AI/ML models into enterprise applications
• Collaborate with enterprise architects to ensure AI solutions align with broader technical strategy and standards
• Design and deploy applications using Cloud Native principles on hyperscaler platforms (AWS, Azure, GCP)

Requirements

• 8+ years' experience as a Software or Solution Architect with focus on application development and scaling solutions for production environments
• 5+ years hands-on experience 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 (e.g. RAG with Vertex AI Search/Vector Search, prompt design, safety policies, observability)
• 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 (e.g., data privacy, model poisoning, adversarial attacks)
• Familiarity with various hyperscaler tools and services

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 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 Vertex AI Pipelines and Cloud Build (or similar)

Skills

* Required skills

Certifications