Job Specifications
Job Description: Senior GCP AI Cloud Engineer (Gen AI & Vertex AI Focus)
Location: USA (Hybrid - San Francisco & Santa Clara)
Experience: 10+ Years
Start Date: Immediately
About The Role
We are seeking a highly seasoned and hands-on Senior GCP AI Cloud Engineer to join our dynamic team. You will be at the forefront of designing, building, and scaling cutting-edge Generative AI solutions on the Google Cloud Platform (GCP). This role requires deep expertise in the GCP AI/ML stack, especially Vertex AI and the Gemini model family, and a proven ability to translate complex product requirements into robust, production-grade systems. You will be a key player in driving our AI strategy and implementation on GCP.
Key Responsibilities
GCP AI Solution Architecture & Development:
Design, build, and deploy sophisticated Generative AI applications on GCP, with a strong focus on AI agents and multi-step reasoning systems built within the Vertex AI ecosystem.
Leverage and fine-tune foundational models, including the Gemini family (e.g., Gemini Pro, Gemini Ultra), using Vertex AI for model training, deployment, and management.
Implement and orchestrate complex LLM workflows using Vertex AI Agent Builder and frameworks like LangChain on GCP for tasks such as Retrieval-Augmented Generation (RAG), summarization, and agentic behaviors.
Architect and implement RAG pipelines on GCP, integrating Vertex AI Search and Conversational AI or custom solutions with vector databases.
GCP Infrastructure & Data Engineering:
Architect and implement end-to-end AI solutions leveraging a wide array of GCP services.
Integrate GCP services for storage (Cloud Storage, BigQuery), serverless logic (Cloud Functions, Cloud Run), data processing (Dataflow, Dataproc), and messaging (Pub/Sub) to create comprehensive, scalable AI pipelines.
Build and maintain reliable and cost-effective data pipelines on GCP to support AI model training and inference.
Rapid Prototyping & Model Evaluation on Vertex AI:
Use Vertex AI Model Garden and Vertex AI Evaluation to quickly conduct experiments, benchmark new LLM models, and analyze their performance, features, and cost.
Implement MLOps practices using Vertex AI Pipelines, ML Metadata, and Model Monitoring to ensure model reliability and performance in production.
Engineering Excellence & Security on GCP:
Employ Infrastructure as Code (IaC) using Terraform to provision and manage GCP resources in a repeatable and scalable manner.
Implement robust security measures for AI systems using GCP’s Identity and Access Management (IAM), Cloud KMS for encryption, and ensure compliance with data governance policies using Data Loss Prevention (DLP) and VPC Service Controls.
Champion software engineering best practices, including version control (Git), CI/CD using Cloud Build, and MLOps principles.
Collaboration & Leadership:
Work closely with Gen AI Leads and cross-functional team members to refine the product backlog, provide technical estimates, and deliver on sprint commitments.
Mentor junior engineers and evangelize best practices in Gen AI development on GCP.
Mandatory Technical Skills & Experience (Primary)
GCP AI/ML & Gen AI Stack:
Vertex AI Platform: Deep, hands-on experience with the core services of Vertex AI, including Model Training, Batch and Online Prediction, Feature Store, Pipelines, and Model Registry.
Gemini & Foundational Models: Production-level experience integrating with the Google Gemini model family (especially Pro 1.x) via API endpoints and within the Vertex AI environment.
LLM Frameworks & Agents: Thorough, implementation-level exposure to building LLM agents using LangChain and/or Vertex AI Agent Builder. A deep understanding of agentic patterns and tools.
Vector Databases: Practical experience with vector databases like Vertex AI Vector Search (preferred), Pinecone, or Chroma for building semantic search systems.
GCP Development & Services:
GCP Core Services: Extensive hands-on experience with core GCP infrastructure and serverless services: Cloud Storage, Cloud Functions, Cloud Run, IAM, BigQuery, and Pub/Sub.
Programming: Excellent programming skills and high proficiency in Python (primary) for Gen AI orchestration and scripting. Secondary proficiency in PySpark or Java is highly desirable.
AI-Assisted Development: A thorough understanding and practical use of AI-assisted development tools like Cursor for rapid prototyping and code generation.
Secondary & Desirable Skills
Infrastructure as Code: Proficiency in Terraform for managing and automating GCP infrastructure.
Multi-Cloud (AWS): Experience with AWS AI/ML services (e.g., SageMaker, Bedrock) is a plus.
Security & Compliance: Strong understanding of GCP security principles, specifically IAM, Cloud KMS, and VPC Service Controls for securing AI models and data.
MLOps: Familiarity with MLOps tools and practices on GCP for model versioning, monitoring, and lifecycle management.
Personal Attributes
Self-Starter: Ability to work autonomo