Google Cloud is the AI-native cloud — Vertex AI, BigQuery ML, and TPU infrastructure give it genuine advantages for teams doing production ML at scale. But the GCP service catalog is dense, and the right combination of Vertex AI, Dataflow, BigQuery, and Cloud Run for your ML architecture is not obvious. Post your GCP job on RemoteAI and hire an engineer who has built ML systems on Google Cloud for real production workloads.
Talent pool
These are the types of GCP engineers available on RemoteAI. New profiles are reviewed by our team.
Services in scope (Vertex AI, BigQuery, GKE, Dataflow, Pub/Sub), current state, and the goal: new build, migration from another cloud, or performance and cost optimization.
Practitioners with hands-on experience designing Vertex Pipelines, BigQuery ML, or Dataflow jobs for production ML — not just GCP web infrastructure engineers.
Look for candidates who understand GCP-specific optimization: BigQuery slot reservations, TPU allocation, and Dataflow runner tuning.
RemoteAI charges nothing on contracts.
Hiring a gcp engineer remotely starts with a clear, well-scoped job post. This guide covers what to include, how RemoteAI's matching works, and what you can expect at each step of the process.
GCP engineers on RemoteAI who specialize in ML and data work across Google Cloud's AI and data stack: designing training and deployment pipelines on Vertex AI (Vertex Pipelines, Model Registry, Prediction Endpoints, AutoML), building large-scale data processing workflows with Dataflow (Apache Beam), running analytics on BigQuery including BigQuery ML for in-database model training, deploying containerized ML services on GKE or Cloud Run, and using Vertex AI Workbench for collaborative ML development. Strong GCP ML engineers also understand the TPU ecosystem — when to use TPUv4 for large-scale training versus GPU VMs — and how to optimize BigQuery query costs at petabyte scale.
A well-structured job post attracts the right gcp engineers and filters out irrelevant applications. Here is the exact process to follow on RemoteAI.
Services in scope (Vertex AI, BigQuery, GKE, Dataflow, Pub/Sub), current state, and the goal: new build, migration from another cloud, or performance and cost optimization.
Practitioners with hands-on experience designing Vertex Pipelines, BigQuery ML, or Dataflow jobs for production ML — not just GCP web infrastructure engineers.
Look for candidates who understand GCP-specific optimization: BigQuery slot reservations, TPU allocation, and Dataflow runner tuning.
RemoteAI charges nothing on contracts.
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GCP services in scope (Vertex AI, BigQuery, GKE, Cloud Run, Dataflow), whether TPU training is relevant, infrastructure-as-code preference (Terraform, Config Connector), and current Cloud IAM and VPC setup.
GCP has the strongest native ML infrastructure — Vertex AI is one of the most complete managed ML platforms, and BigQuery ML enables SQL-native model training at scale. GCP also has the best TPU access for large-scale LLM training. AWS has a broader service catalog and larger enterprise adoption. For teams building AI-native products, GCP's ML stack is genuinely differentiated.
Free for one job a month (open 14 days; jobs from companies without a verified website are reviewed first, paid plans for more). No commission on contracts.
Vertex AI Pipelines and TPU training specialists usually charge more. Rates depend on seniority, country and scope. Put a budget or rate range in your job post and ask candidates for their rate in the first message; RemoteAI takes no placement fee on what you pay them.
Sign up free and describe the GCP services, ML workload type, and architecture problem. RemoteAI matches your post to GCP engineers with relevant Vertex AI and data stack experience.