Vertex AI
Model Garden, tuning, evaluation, pipelines, feature store, endpoints.
ML engineers and architects who take Gemini-based use cases from notebook to governed production on Google Cloud. Delivered through staff augmentation, dedicated squads or fractional architecture, sourced through the Google Cloud practitioner communities and vetted by a senior peer.
Modules
Model Garden, tuning, evaluation, pipelines, feature store, endpoints.
Enterprise search and agents over Workspace and third-party sources.
Multi-agent systems, tool use, deployment on Agent Engine.
BigQuery ML, vector search, data governance with Dataplex.
Checked against the vendor's public credential registry where available.
FAQ
Mostly outside job boards. We source through the Google Cloud practitioner communities: user groups, community forums, meetups and conference speakers, with priority to recognised community contributors. Lead time depends on how scarce the profile is; we give you a realistic estimate when we qualify the brief.
Every profile passes three checks: a technical interview led by a senior peer on the same Google Cloud stack, verification of vendor credentials against the official registry where one exists, and reference calls on past enterprise deliveries. Profiles that fail any step are not presented.
Three models: staff augmentation (an expert embedded in your team, billed on a daily rate), a dedicated squad (architect plus engineers delivering an agreed scope), and a fractional AI architect (two to eight days a month for design authority and governance).
Yes. Our Google Cloud profiles design for EU regions, VPC Service Controls, CMEK and Assured Workloads where required, and document residency choices for your DPO.