GCP ML Engineer (Vertex AI)

Description: Hybrid 3 days/week in Concord, CA Our client seeks a Senior/Lead Generative AI Platform Engineer to design, implement, and scale enterprise AI/ML platforms across hybrid cloud. The role will architect secure, cost-optimized, and high-performance environments for LLMs and classical ML, spanning compute, storage, networking, and MLOps. The engineer will drive orchestration, observability, and reliability for large-scale workloads while enabling data science through robust tooling and pipelines. Due to client requirements, applicants must be willing and able to work on a w2 basis. For our w2 consultants, we offer a great benefits package that includes Medical, Dental, and Vision benefits, 401k with company matching, and life insurance. Rate: $84.00 to $94.00/hr. w2 JN -08 #LI-JS1 Responsibilities: • Design and build scalable AI/ML platform components across on-prem and public cloud environments including Google Cloud Platform, GKE, OpenShift AI (RHOAI), and IBM Cloud Pak for Data for multi-tenant operations. • Architect hybrid CPU/GPU grid computing, object and high-performance storage, and low-latency networking to support demanding AI workloads. • Deploy and manage enterprise AI tooling including H2O AI platforms (Driverless AI) for automated model development and data virtualization. • Implement and administer Run:ai to optimize GPU/CPU utilization with high-throughput and low-latency scheduling for training and inference. • Operationalize end-to-end LLM and classical ML pipelines using Vertex AI with CI/CD, automated validation, and observability. • Build and maintain vector databases, chunking, and embedding strategies to enable RAG-based applications. • Deploy and manage Istio service mesh for secure, observable, and resilient service-to-service and AI API communication. • Apply SRE practices including circuit breakers, autoscaling, and reliability patterns to ensure system health. • Lead cross-functional architecture discussions and influence platform direction and standards. Experience Requirements: • 5+ years of Python programming and 3+ years of MLOps experience in production environments. • 5+ years with Big Data platforms such as BigQuery or Hadoop and 3+ years with PySpark. • 2+ years building APIs, preferably with FastAPI, and integrating with Google Cloud Platform/Azure or API gateways. • Expertise with containerization and orchestration including Google Cloud Platform, GKE, Red Hat OpenShift, and Docker with service mesh integration. • Hands-on experience with Vertex AI for pipelines and IBM Cloud Pak for Data for enterprise data management. • Strong understanding of LLMs and vector databases, plus working knowledge of AutoML platforms such as H2O Driverless AI or DataRobot. • Advanced IaC skills with Terraform, Helm, or Ansible for automating cloud landing zones and cluster configurations. • Proficiency in PySpark, Hadoop, or BigQuery for high-throughput data processing and vectorization pipelines. • Experience with RAG, prompt orchestration, fine-tuning, and safety/guardrails (preferred). • Deep understanding of GPU/CPU orchestration and high-performance storage for AI workloads (preferred). • Ability to lead cross-functional architecture forums and influence senior stakeholders (preferred). • Familiarity with Agile methodologies and enterprise project management tools (preferred). 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