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Remote Pinecone Jobs (NOW HIRING)

Remote / Hybrid - Chicago preferred Employment Type: Contract / Full-Time Reports To: GCP Technical ... Implement RAG pipelines leveraging Vertex Matching Engine or Pinecone . * Collaborate with ...

Role - AI ML Engineer || Sunrise , FL || Remote Experience Required - 8+ Years Must Have Technical ... vector databases (FAISS, Pinecone, Chroma) • Experience deploying models using APIs ...

We work with companies like Vercel, Pinecone, Perplexity, and Replit to help them price smarter ... For remote roles, we ask you to join us in person 1x per quarter. Our values, customer centricity ...

AI Data Lead

$180K - $220K/yr

This remote AI Data Lead role will enable you to join A forward-thinking engineering culture that ... Qdrant, Pinecone, Weaviate, pgvector), including tuning for performance and recall * Experience ...

Overview Location * US-Remote or Marlton, NJ area Job Title * Software Engineer Salary ... Experience with vector databases or embeddings systems (Pinecone, Weaviate, Elasticsearch, etc.

... is a remote-first company. We currently hire in most U.S. states, with the exception of Hawaii ... Pinecone, Weaviate, FAISS, Milvus). * Deep understanding of AIOps, data pipelines, evaluation, and ...

We offer a remote or hybrid work arrangement based on your preference and ability to perform ... databases (Pinecone, FAISS, Azure AI Search). * Familiarity with RAG architectures, prompt ...

... is a remote-first company. We currently hire in most U.S. states, with the exception of Hawaii ... Pinecone, Weaviate, FAISS, Milvus). * 3+ years in working with MLOps, data pipelines, evaluation ...

Senior AI Engineer

$170K - $200K/yr

Own and improve RAG pipelines across multiple Pinecone namespaces, including chunking strategy ... Availability to support critical launches or events when needed Location * Full-time, remote (US ...

3-8 Years of Industry Experience | Remote | High-Impact About the Role: We're looking for an ... Help manage vector databases and semantic search infrastructure (e.g., Pinecone, FAISS, Vertex ...

Remote, United States Compensation : Up to $240,000 + Equity We're partnered with a high-growth ... Strong background in RAG, embeddings, reranking, and vector databases (e.g., Pinecone, FAISS ...

This is a remote opportunity and we would be interested in applicants from USA time zones only at ... Experience with vector databases or retrieval pipelines (Pinecone, Weaviate, ChromaDB, pgvector, or ...

Data Engineer with AI - Remote

Boston, MA · On-site +1

$124K - $149K/yr

Vector DBs (FAISS, pgvector, Pinecone), RAG frameworks (LangChain/LlamaIndex). * IaC (Terraform), security/compliance (PII handling, data masking). * Experience interfacing with BI tools (Power BI ...

The position is FULLY REMOTE , based in Latin America. Professional English proficiency (B2/C1 ... Hands-on experience with vector databases (e.g., Pinecone , FAISS ). * Experience building AI ...

This is a remote opportunity and we are looking for candidates from the U.S. The Opportunity ... Experience with vector databases or retrieval pipelines (Pinecone, Weaviate, ChromaDB, Qdrant ...

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Remote Pinecone information

What is the difference between Remote Pinecone vs Remote Data Scientist?

AspectRemote PineconeRemote Data Scientist
Required CredentialsExperience with vector databases, cloud platforms, programming (Python, SQL)Statistics, machine learning, programming (Python, R)
Work EnvironmentRemote, tech-focused, collaborative teamsRemote, research and analysis-driven
Industry UsageAI, machine learning, data infrastructureAnalytics, AI, research, business intelligence
Search & Comparison IntentUnderstanding roles in data infrastructure and vector databasesComparing data science roles and skills

Remote Pinecone roles focus on managing vector databases and infrastructure, requiring technical skills in cloud platforms and programming. Remote Data Scientists analyze data, build models, and derive insights, often with a background in statistics and machine learning. While both roles are remote and tech-oriented, they serve different functions within data-driven organizations.

More about Remote Pinecone jobs
What cities are hiring for Remote Pinecone jobs? Cities with the most Remote Pinecone job openings:
What are the most commonly searched types of Pinecone jobs? The most popular types of Pinecone jobs are:
What states have the most Remote Pinecone jobs? States with the most job openings for Remote Pinecone jobs include:
Infographic showing various Remote Pinecone job openings in the United States as of June 2026, with employment types broken down into 97% Full Time, and 3% Contract. Highlights an 70% Physical, 5% Hybrid, and 25% Remote job distribution.
GCP Gemini AI Developer

GCP Gemini AI Developer

CoSourcing Partners

Chicago, IL • On-site, Remote

Other

Posted 7 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)OutcomeWhat Success Looks LikeMeasurement1. Gemini-Powered Solutions DeployedDesign, 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 ArchitectureBuild 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 WorkflowsImplement 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 EnablementPartner 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 OptimizationMonitor, 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