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Vertex Ai Jobs (NOW HIRING)

Paramount

Senior Applied AI Engineer

Manhattan, NY · Hybrid

$115K - $157K/yr

Cloud & Platform Engineering (Vertex AI) Leverage Google Cloud Vertex AI to build scalable, production-grade AI systems, including: o Model Garden o Vertex AI Training, Tuning (LoRA/QLoRA), and ...

Vings Technologies

AI/ML Architect

San Jose, CA · On-site

$74.75 - $96/hr

Design, build, and manage automated data ingestion, transformation, and validation pipelines using services like Kubeflow Pipelines and Vertex AI Pipelines . * Feature Engineering: Implement and ...

New

Vings Technologies

AI/ML Architect

San Jose, CA · On-site

$74.75 - $96/hr

Design, build, and manage automated data ingestion, transformation, and validation pipelines using services like Kubeflow Pipelines and Vertex AI Pipelines . * Feature Engineering: Implement and ...

New

Noblesoft Technologies

AI Architect

Paramus, NJ · On-site

Vertex AI for model lifecycle management * Google Agent Development Kit (ADK) for intelligent agents * Google Workspace integrations (Docs, Sheets, Gmail, Drive, Meet) * Architect solutions using ...

Paramount

Senior Applied AI Engineer

Manhattan, NY · On-site

$115K - $157K/yr

Cloud & Platform Engineering (Vertex AI) • Leverage Google Cloud Vertex AI to build scalable, production-grade AI systems, including: o Model Garden o Vertex AI Training, Tuning (LoRA/QLoRA), and ...

Trinity Technology Solutions

AI Engineer

San Jose, CA · On-site

Experience with Al ML search and data services within GCP ecosystem such as GCP Vertex AI Vertex AI Vector Search Gemini Cloud Run Cloud SQL etc * Experience with agent frameworks such as Google ADK ...

Marencor

AI Solutions Architect

New York, NY · On-site

$69 - $90.75/hr

Architect end-to-end ML pipelines using Airflow, and MLFlow or Vertex AI * Build containerized model inference systems deployed on Kubernetes with AppDynamics and observability * Full ownership of ...

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How much do vertex ai jobs pay per hour?

As of Aug 22, 2026, the average hourly pay for vertex ai in the United States is $22.50, according to ZipRecruiter salary data. Most workers in this role earn between $17.31 and $24.52 per hour, depending on experience, location, and employer.

What is Vertex AI?

Vertex AI is a managed machine learning (ML) platform provided by Google Cloud that enables users to build, deploy, and scale ML models efficiently. It integrates various Google Cloud services and tools for data preparation, model training, evaluation, deployment, and monitoring, all within a unified interface. Vertex AI supports both custom and AutoML models, making it accessible for users with varying levels of ML expertise. It also offers MLOps features for workflow automation and collaboration, streamlining the entire ML lifecycle.

How do Vertex AI engineers typically collaborate with data scientists and business stakeholders on machine learning projects?

Vertex AI engineers frequently work closely with data scientists to streamline the deployment and scaling of machine learning models on Google Cloud. They help bridge the gap between model development and production, ensuring that models are robust, scalable, and aligned with business objectives. Collaboration often involves regular meetings to clarify requirements, sharing best practices for model monitoring, and integrating models with existing business systems. Effective communication skills and a clear understanding of both technical and business priorities are essential for success in this role.

What are the key skills and qualifications needed to thrive as a Vertex AI specialist, and why are they important?

To excel as a Vertex AI Specialist, you need a solid background in machine learning, data science, and cloud computing, often supported by a degree in computer science or a related field. Familiarity with Google Cloud Platform, Vertex AI tools, Python programming, and relevant certifications such as Google Professional Machine Learning Engineer are typically required. Analytical thinking, problem-solving, and strong communication skills help you collaborate with teams and explain complex AI solutions to stakeholders. These skills are vital for building, deploying, and optimizing machine learning models efficiently in cloud environments.

Does Vertex Ai have remote jobs?

Vertex AI offers remote job opportunities for roles such as data scientists and machine learning engineers. These positions often require proficiency with cloud platforms, programming skills, and collaboration tools, and may be available on a flexible or fully remote basis depending on the role and company policies.
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Vertex Ai jobs near you
Infographic showing various Vertex Ai job openings in the United States as of August 2026, with employment types broken down into 76% Full Time, 21% Part Time, and 3% Contract. Highlights an 64% Physical, 4% Hybrid, and 32% Remote job distribution, with an average salary of $46,809 per year, or $22.5 per hour.

GCP Gemini AI Developer

Co-Sourcing Partners

Chicago, IL • On-site

Full-time

Re-posted 21 days ago

Job description

Job Title: GCP Gemini AI Developer (3-5 Years Experience)
Location: Remote / Hybrid - Chicago preferred
Employment Type: Contract / Full-Time
Reports To: GCP Technical Lead / AI Program Manager
Purpose
The GCP Gemini AI Developer will design, build, and deploy intelligent applications leveraging Google Cloud's Gemini models and Vertex AI platform. This role exists to operationalize advanced GenAI capabilities - including natural language understanding, multimodal reasoning, and generative automation - within scalable, secure, and production-ready cloud environments.
The developer will work hands-on across data engineering, AI model orchestration, and API integration to create AI-driven business solutions that reduce manual effort, enhance decision-making, and unlock measurable value from enterprise data.
Key Performance Outcomes (6-12 Months) Outcome What Success Looks Like Measurement 1. Gemini-Powered Solutions Deployed Design, develop, and deploy at least two Gemini-based AI solutions (e.g., document summarization, chat agent, or data extraction automation) using Vertex AI + Gemini APIs. Delivered to production with >90% accuracy and <2s response time. 2. Scalable Cloud Architecture Build a modular AI microservices framework using Cloud Run / Cloud Functions with integrated authentication, logging, and monitoring. Reusable components adopted in at least 3 future use cases. 3. RAG / Context-Aware Workflows Implement Retrieval-Augmented Generation (RAG) pipelines combining Gemini + BigQuery or vector databases for knowledge grounding. Demonstrated 25% reduction in hallucination or response variance. 4. Cross-Team Enablement Partner with Data, Automation, and AppDev teams to integrate Gemini AI into existing business workflows (e.g., UiPath, Power Platform, or ServiceNow). Minimum of 2 successful integrations with documented ROI. 5. Continuous Optimization Monitor, retrain, and improve AI models via Vertex AI pipelines and Model Monitoring. Demonstrated 15% performance gain over baseline models. Core Responsibilities
  • Design and deploy Gemini 1.5 Pro/Flash integrations via Vertex AI and Generative AI Studio.
  • Build serverless APIs and backend services for AI workflows using Cloud Run, Functions, or App Engine.
  • Develop data ingestion and preprocessing pipelines using BigQuery, Dataform, and Pub/Sub.
  • Apply prompt engineering and parameter tuning to improve generative model accuracy.
  • Implement RAG pipelines leveraging Vertex Matching Engine or Pinecone.
  • Collaborate with automation and data teams to embed AI into existing business processes.
  • Maintain compliance with security, privacy, and model governance standards.

Technical Environment
Core Google Cloud Services
  • Vertex AI, Generative AI Studio, Gemini API
  • BigQuery, BigQuery ML, Dataform
  • Cloud Run, Cloud Functions, Cloud Storage
  • Pub/Sub, Secret Manager, IAM, Cloud Build

Programming Stack
  • Python or TypeScript (Google Cloud SDKs, google-generativeai, aiplatform)
  • FastAPI / Flask / Node.js
  • LangChain / LlamaIndex for orchestration
  • SQL, Pandas, and Jupyter for data prep

Complementary Tools
  • Terraform (IaC)
  • GitHub / GitLab CI/CD
  • Vertex AI Pipelines & Model Registry
  • Vector DB (Vertex Matching Engine, Pinecone, or Weaviate)

Ideal Profile
  • 3-5 years hands-on GCP development experience with AI/ML exposure
  • Strong working knowledge of Vertex AI, Gemini models, and RAG pipeline design
  • Demonstrated ability to move AI prototypes into production
  • Strong communicator, able to collaborate across automation, data, and cloud teams
  • Curious problem-solver passionate about applied AI innovation

Success Metrics
  • Speed to Delivery: End-to-end deployment within 8-10 weeks per use case
  • Model Effectiveness: >90% accuracy or relevance rating from business stakeholders
  • Scalability: Framework reused for ≥3 additional AI initiatives
  • Business Impact: 25%+ improvement in productivity or efficiency from deployed use cases

Apply