Senior/Lead Generative AI Platform Engineer

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 -032026-105708

Responsibilities

• Design and build scalable AI/ML platform components across on-prem and public cloud environments including GCP, 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 GCP/Azure or API gateways.
• Expertise with containerization and orchestration including GCP, 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).
  
  

Education Requirements