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Hire Vetted GCP Engineers for AI Infrastructure

Find cloud engineers with Google Cloud AI expertise — Vertex AI pipelines, BigQuery ML, Gemini API integration, and scalable data infrastructure on GCP.

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Vertex AI Engineer GCP Data Engineer BigQuery ML Developer Cloud AI Architect Gemini API Developer
MLOps & AI Infrastructure

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Teddy

Teddy

Senior
Cambodia — Phnom Penh (UTC+7) Contract, Freelance
MLOps ml (machine learning)
python docker pyspark kubernetes aws +5
Open profile
Khandu

Khandu

Junior
India — Kolkata (UTC+5.5) Full-time, Contract-to-hire
Automation & AI Workflows AI & Machine Learning Engineers
python tensorflow rag keras gcp +5
Open profile
Firas

Firas

Mid-Level
Tunisia — Tunis (UTC+1) Full-time, Part-time
AI & Machine Learning Engineers MLOps & AI Infrastructure
python pytorch computer vision gcp hugging face +5
Open profile
Moises

Moises

Senior
Colombia — Bogota (UTC-5) Part-time, Freelance
MLOps & AI Infrastructure AI & Machine Learning Engineers
kubernetes aws python docker ci/cd +5
Open profile
Tal

Tal

Senior
Ireland — Dublin (UTC+1) Full-time
AI & Machine Learning Engineers AI Product & Strategy
python rag aws sql ai agents +5
Open profile
Saad Malik

Saad Malik

Mid-Level
Pakistan — Karachi (UTC+5) Full-time, Part-time
Automation & AI Workflows NLP & Prompt Engineering
python langchain rag docker prompt engineering +5
Open profile
Dexter

Dexter

Senior
United States — New York (UTC-4) Full-time, Part-time
AI & Machine Learning Engineers NLP & Prompt Engineering
python pytorch tensorflow langchain aws +5
Open profile
Manal

Manal

Mid-Level
France — Paris (UTC+2) Full-time, Freelance
AI Implementation & Consulting AI & Machine Learning Engineers
python gcp langchain rag sql +5
Open profile
Eduardo

Eduardo

Mid-Level
Australia — Perth (UTC+8) Full-time, Freelance
AI & Machine Learning Engineers NLP & Prompt Engineering
python rag prompt engineering openai api pandas +5
Open profile
Kamalam

Kamalam

Junior
India — Kolkata (UTC+5.5) Part-time, Freelance
AI & Machine Learning Engineers NLP & Prompt Engineering
python pandas numpy computer vision prompt engineering +5
Open profile

Hiring guide

Why companies hire GCP experts

GCP offers some of the most powerful AI infrastructure available, with Vertex AI for end-to-end ML, BigQuery ML for in-database analytics, and direct access to Google's Gemini models. Engineers who know GCP can accelerate AI development on Google Cloud significantly.

Popular projects

  • Build and deploy ML pipelines on Vertex AI end-to-end
  • Integrate Gemini API into your Google Cloud product
  • Run predictive models directly in BigQuery without moving data
  • Process large-scale AI training datasets with Dataflow

What to look for

  • Vertex AI and BigQuery ML production experience
  • Knowledge of GCP IAM, VPC, and security for ML workloads
  • Experience with Dataflow or Spark on Dataproc for large data processing

Frequently asked questions

What makes GCP good for AI workloads?

GCP offers TPUs (Google's custom AI chips), Vertex AI's end-to-end MLOps platform, BigQuery for large-scale analytics, and native access to Google's Gemini model family — making it a strong choice for both training and production inference.

Can GCP engineers also work with Gemini and other Google AI models?

Yes. Engineers familiar with GCP often have experience with the Vertex AI Generative AI APIs and the Gemini model family, including multimodal capabilities.

How does GCP compare to AWS for an AI startup?

GCP is often chosen for its BigQuery analytics capabilities, Vertex AI's clean MLOps tooling, and access to TPUs for large-scale training. AWS has broader service breadth and a larger ecosystem. Many teams choose GCP when BigQuery is already their analytics platform.

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