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Remote Google Technology information

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$90K

$118.4K

$158.5K

How much do remote google technology jobs pay per year?

As of Aug 23, 2026, the average yearly pay for remote google technology in the United States is $118,371.00, according to ZipRecruiter salary data. Most workers in this role earn between $102,000.00 and $126,500.00 per year, depending on experience, location, and employer.

What is a remote Google technology job?

Remote Google technology jobs are positions that involve working with Google’s suite of technologies and platforms, such as Google Cloud, Google Workspace, and various Google APIs, while performing all job duties remotely. These roles can include software engineering, cloud architecture, technical support, and project management, among others. Employees in these jobs collaborate virtually using digital tools and are not required to work from a Google office or any specific location. This flexibility allows professionals to work from anywhere while leveraging Google’s cutting-edge technologies.

What skills and qualifications are needed to thrive in a remote Google technology role?

To excel in a remote Google technology role, you need a strong background in cloud computing, programming (such as Python or Java), and familiarity with Google Cloud Platform (GCP) services, often supported by a relevant degree or certifications like Google Cloud Certified. Mastery of tools like Google Cloud Console, BigQuery, Kubernetes, and collaboration platforms such as Google Workspace is typically required. Self-motivation, proactive communication, and strong problem-solving skills are essential soft skills for remote work success. These competencies ensure effective project delivery, seamless teamwork, and the ability to adapt to evolving cloud technologies in a distributed work environment.

What are common challenges faced by professionals in remote Google technology roles, and how can they be addressed?

Professionals in remote Google technology roles often encounter challenges such as coordinating effectively with distributed teams, staying updated with rapidly evolving Google platforms, and managing time across different time zones. To address these, it’s important to leverage collaboration tools like Google Meet, Chat, and Drive, and set clear communication expectations with teammates. Regularly participating in virtual training sessions and Google developer communities also helps in staying current and connected, promoting both individual growth and team success.

What is the difference between Remote Google Technology vs Remote Cloud Support Specialist?

AspectRemote Google TechnologyRemote Cloud Support Specialist
Required CredentialsGoogle certifications, technical degreesCloud platform certifications (AWS, Azure, Google Cloud)
Work EnvironmentRemote, tech-focused teams, Google productsRemote, cloud service providers, client support
Employer & Industry UsageGoogle, tech companies, startupsCloud providers, IT services, enterprise clients
Common Search & ComparisonYesYes

Remote Google Technology roles focus on supporting and implementing Google products and services, often requiring Google-specific certifications. Remote Cloud Support Specialists handle cloud platform issues across providers like Google Cloud, AWS, or Azure, with certifications in those platforms. Both roles are remote, tech-oriented, and serve similar industries, but differ in platform specialization and certification requirements.

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What are the most commonly searched types of Google Technology jobs?

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What states have the most Remote Google Technology jobs?

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What job categories do people searching Remote Google Technology jobs look for?

The top searched job categories for Remote Google Technology jobs are:

Remote Google Technology jobs near you
Infographic showing various Remote Google Technology job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 17% Part Time, and 4% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution, with an average salary of $118,371 per year, or $56.9 per hour.

Senior Generative AI Engineer (Google Gemini & Vertex AI) - Remote

DivIHN Integration Inc

San Diego, CA • Remote

$51 - $55/hr

Contractor

Posted 4 days ago

Job description

DivIHN (pronounced “divine”) is a CMMI ML3-certified Technology and Talent solutions firm. Driven by a unique Purpose, Culture, and Value Delivery Model, we enable meaningful connections between talented professionals and forward-thinking organizations. Since our formation in 2002, organizations across commercial and public sectors have been trusting us to help build their teams with exceptional temporary and permanent talent.

Visit us at https://divihn.com/find-a-job/ to learn more and view our open positions.

 
Please apply or call one of us to learn more

For further inquiries about this opportunity, please contact one of  our Talent Specialists, Justeen at (224)-394-4903
 
Title: Senior Generative AI Engineer (Google Gemini & Vertex AI) - Remote
Duration: 6 Months with possible for extension
Location: Remote
 
Candidates must be available to work West Coast (PST) hours.
 
Only W2 candidates are eligible for this position. Third-party or C2C candidates will not be considered.
 
Day to Day Responsibilities
  • RAG & Prompt Engineering: Craft and refine effective prompts for RAG, grounding, and context tuning to achieve optimal AI performance in product development. 
  • High-Performance API Engineering: Develop asynchronous microservices (FastAPI) using Server-Sent Events (SSE) or WebSockets to stream real-time LLM responses to front-end UIs without backend timeouts. 
  • Vector Database Infrastructure: Design, develop, and implement robust Vector Databases using LLMs and modern retrieval technologies to capture information from diverse engineering sources (PDFs, design docs, regulatory guidelines). 
  • Data Extraction & Structuring Pipelines: Build and optimize pipelines to extract and structure multi-modal data (tables, text, images) from unstructured documents for LLM training, grounding, and runtime query execution. 
  • LLM Fine-Tuning & Training: Fine-tune and train generative AI models using client's engineering data and domain knowledge to create high-accuracy, domain-specific models. 
  • GenAI Application & Tool Development: Design and implement scalable backend APIs (FastAPI/REST) and UI integration interfaces so internal engineers can query knowledge bases and analyze data. 
  • Automated Requirements Generation: Develop backend functionalities to automatically generate technical requirements from design documents, user stories, and system specification files. 
  • Documentation & Knowledge Transfer: Thoroughly document architecture, code, REST endpoints, and model training procedures to enable seamless knowledge transfer to client's internal teams. 
  • Cross-Functional Collaboration: Partner closely with Subject Matter Experts (SMEs), System Engineers, and V&V Test teams to optimize AI-powered workflows. 
  • AI Guardrails, MLOps & Cost Governance: Implement hallucination checks, PII masking, and guardrails (e.g., NeMo Guardrails) for medical device context. Track token usage, latency, and costs using LangSmith or Vertex AI monitoring.
  • Collaborate closely with engineers and cross-functional teams to design and develop AI solutions.
  • Develop and integrate Generative AI applications and APIs.
  • Work with large datasets, document processing, and data pipelines.
  • Participate in problem-solving, solution design, and technical discussions.
  • Communicate effectively with team members and stakeholders.
Must-Have Qualifications :
  •  Relevant Experience & STEM Foundation: 4+ years of professional software/ML engineering experience, with a dedicated AI/ML focus in the last 1–2 years.
  •  Google Gemini / Vertex AI (Non-negotiable): Hands-on experience with the Gemini model family and Vertex AI, including deployment, grounding, and integration into production AI services.  Hands-on experience with containerization (Docker) and deploying services via Cloud Run or GKE (Kubernetes).
  • Languages & AI Libraries: Proficiency in Python and modern ML/AI frameworks (PyTorch, LangChain, LangSmith) for building autonomous LLM agents, tools, and RAG pipelines.
  • Agent Building & Tool Calling: Proven experience building AI/LLM agents and tool-calling systems in Python against unstructured, multi-source data.
  • Context Engineering & RAG: Expertise in RAG pipelines, prompt engineering, context tuning, grounding, and Vector Databases (e.g., Milvus, Postgres/Pgvector). Clear understanding of advanced RAG architecture including Hybrid Search (Vector + Keyword), Re-ranking models, and semantic caching.
  • Unstructured Data Handling (Non-negotiable): Demonstrated ability to ingest, clean, extract, and structure text, tables, and images from unstructured documents (PDFs, design docs, regulatory files) for LLM training and usage.
Role Information
  • Generative AI Engineer role.
  • Experience in API development.
  • Hands-on experience with Google Gemini and Vertex AI.
  • Knowledge of Agentic AI frameworks and architectures.
  • Strong Python development skills, including experience with LangChain and related AI frameworks.
  • Open to candidates from any industry domain.
Required Skills :
  1. Full Stack Engineering — Building applications for AI-powered services (Backend APIs + Front-End Integration).
  2.  Generative AI & LLM Platforms — 2+ years building RAG pipelines & LLM apps using Python, LangChain, and LangSmith.
  3. Agent Building & Unstructured Data — Building AI agents/tool-calling systems in Python and handling unstructured, multi-source data extraction (PDFs, docs).
  4. Strong hands-on experience in Artificial Intelligence and Generative AI
  5. Python programming expertise.
  6. Experience with LangChain and Agentic AI frameworks.
  7. Understanding data handling to pull data from PDFs, design docs, regulatory files
Preferred Skills :
  1. Direct experience architecting and serving custom REST APIs.
  2. Experience in regulated / compliance-sensitive domains (e.g., healthcare / medical device guidelines like FDA, ISO 13485).
  3. Experience integrating multiple LLM APIs beyond a single provider (OpenAI, Bedrock, Claude, or similar).
Additional Preferred Skills 
  • Direct experience designing and deploying high-throughput REST APIs (e.g., FastAPI/Flask).
  • Familiarity with medical device development regulations and compliance (e.g., FDA guidelines, ISO 13485).
  • Experience integrating multiple LLM APIs beyond a single vendor (e.g., OpenAI, AWS Bedrock, Anthropic Claude).
  • Front-end development/integration experience for UI design (e.g., Streamlit, Gradio, React/Next.js integration).
Education Requirements:
  • Minimum Bachelors in Software/Computer/IT/Systems/Biomedical Engineering + 3 years
Required Testing:
  • Technical evaluation of Python proficiency, RAG architecture concepts, and API/Agent design.
Software Skills Required:
  • Languages: Python (AsyncIO, OOP), SQL.
  • AI & Agent Frameworks: PyTorch, LangChain, LangSmith, Vertex AI SDK.
  • API & Web Frameworks: FastAPI, Flask, REST APIs, Server-Sent Events (SSE).
  • Databases & Search: Milvus, Pgvector, Qdrant, Redis (caching).
  • Front-End Integration: Streamlit, Gradio, basic React/Next.js.
  • Testing & Guardrails: pytest, JUnit, LangSmith evaluation, NeMo Guardrails.
  • DevOps & Cloud: Docker, GCP (Vertex AI, Cloud Run, GKE), Git.
Required Certifications:
  • Professional certifications specific to AI/ML (e.g., Certified AI Professional / CAIP, Google Cloud ML Engineer) considered a plus.
Interview 
  • Number of Interviews: 1,
  • Web Conference (Zoom/ TEAMs) 
  • Live problem-solving and technical exercise during the interview.

About us:
DivIHN, the 'IT Asset Performance Services' organization, provides Professional Consulting, Custom Projects, and Professional Resource Augmentation services to clients in the Mid-West and beyond. The strategic characteristics of the organization are Standardization, Specialization, and Collaboration.

DivIHN is an equal opportunity employer. DivIHN does not and shall not discriminate against any employee or qualified applicant on the basis of race, color, religion (creed), gender, gender expression, age, national origin (ancestry), disability, marital status, sexual orientation, or military status.