Hire developers · AI & Cloud
AI & Cloud
Hire GCP engineers
The smallest of the three pools in India, and concentrated around data and ML work rather than general infrastructure.
What they are hired to build
The work, before the CV.
A role written against the work gets a better shortlist than a role written against a stack. These are what GCP engineers are usually brought in to do.
Roles we recruit: GCP cloud engineer · Data platform engineer (GCP) · ML infrastructure engineer.
- BigQuery-centred data platforms
- GKE workloads
- Vertex AI and ML infrastructure
- Analytics estates built on Google's data stack
The technical screen
What the conversation actually covers.
Every candidate has a real technical conversation with an engineer who has done the job, and you get written evidence per competency. In GCP, that conversation covers the following.
Usually assessed alongside: BigQuery, Terraform, Kubernetes, Python.
- BigQuery cost and partitioning — the bill is the tell
- IAM and the project/folder hierarchy at organisation scale
- GKE operation, not just deployment
- Where they have used managed services versus rebuilt them
The senior GCP pool in India is small. Expect a longer search than the equivalent AWS role, and we will give you that timeline before you start.
Three ways to bring one on
The route changes; the screening does not.
The same three routes apply whichever stack you are hiring for. Which one fits depends on how long you need the person and whether the headcount is approved.
Permanent placement
Companies hiring engineering and data roles
One fee on joining, with a 90-day replacement guarantee.
- Typical timeline
- 3–8 weeks to offer
Contract staffing & staff augmentation
Companies with budget but no headcount
We employ them, you direct them. Monthly, for when the need is real but headcount is not approved.
- Typical timeline
- Monthly, 3-month minimum
Contract to hire
Companies hiring into a new or unclear role
Three to six months as a contractor, then convert. The honest option when neither side is sure.
- Typical timeline
- 3–6 months to conversion
Or have us build it instead
If the need is the work, not the headcount.
AI & Data
Data platform engineering
Warehouse, pipelines and data quality — the foundation most AI projects discover they are missing.
AI & Data
MLOps / LLMOps platform setup
Getting models into production properly, and keeping them there.
Cloud & DevOps
Kubernetes & container platform
Platform engineering for teams that have outgrown running containers by hand.