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Pinecone Jobs (NOW HIRING)

Familiarity with Vector Databases (Pinecone, Weaviate, FAISS, Milvus, etc.). * Knowledge of ML pipelines, APIs, and prompt engineering. Excellent problem-solving, communication, and collaboration ...

Expertise in Python, vector databases (Elastic, Milvus, Pinecone, etc.), embeddings, and chunking strategies * Hands-on experience with Kubernetes, Docker, and scalable API deployment ...

Gen. AI Engineer

Fort Worth, TX · On-site

$100K - $160K/yr

Experience building Retrieval-Augmented Generation (RAG) solutions and working with vector databases such as Pinecone, Weaviate, Chroma, Milvus, or Azure AI Search. * Experience with Agentic AI ...

Python Developer

Dallas, TX · On-site

$49.75 - $68.50/hr

... Pinecone * Portfolio of LLM applications and sample projects * 2+ years of NLP experience using tools such as NLTK, SpaCy, and Beautiful Soup * 1+ years of LLM experience building RAG systems at ...

Manage vector database operations (e.g., Pinecone/Weaviate) for GenAI search augmentation * Integrate with orchestration layers for multi-agent systems using LangChain, CrewAI Required Qualifications ...

Senior Agentic AI Developer

Coppell, TX · On-site

$50.75 - $67/hr

Experience with RAG architectures, embeddings, vector databases (Pinecone, Weaviate, Chroma, pgVector), prompt engineering, and AI orchestration. * Hands-on experience with AI development tools such ...

Manage datasets, preprocess data, and implement RAG with vector databases (FAISS, Chroma, Pinecone). * Automate training workflows using ML flow, Weights & Biases, and Ray. * Deploy models using ...

Implement and maintain vector databases (e.g., Pinecone, Neo4j, Weaviate, Chroma, or Azure Cognitive Search). * Integrate open-source and proprietary LLMs (e.g., GPT, Claude, Llama) into the ...

Sr. AI/ML Engineer - Onsite

Jersey City, NJ · On-site

$109K - $149K/yr

Fine-tune and deploy LLMs integrated with vector databases (FAISS, Pinecone, ChromaDB). Detailed Description: * Experienced AI/ML Engineer with expertise in Machine Learning, Deep Learning, NLP ...

Showing results 41-60

Pinecone information

What is a Pinecone?

A Pinecone job typically refers to working with Pinecone, a vector database designed for machine learning and AI applications. Roles can range from engineering positions that focus on optimizing search and retrieval to data science roles that work with embeddings. Pinecone jobs often require knowledge of machine learning, data indexing, and scalable infrastructure.

What are the typical responsibilities and challenges for an engineer working with Pinecone vector databases?

Engineers working with Pinecone typically focus on building, maintaining, and scaling vector search solutions that power features like semantic search and recommendations. Daily tasks often include integrating Pinecone with other data systems, optimizing indexing and query performance, and ensuring high availability. Common challenges involve efficiently handling large-scale data, managing latency requirements, and troubleshooting distributed system issues. Collaboration with data scientists and backend engineers is frequent to ensure seamless model integration and real-time data flows.

What are the key skills and qualifications needed to thrive as a Pinecone engineer, and why are they important?

To thrive as a Pinecone Engineer, you need a solid background in computer science, experience with vector databases, and proficiency in programming languages such as Python or Java. Familiarity with Pinecone's vector database platform, cloud infrastructure (like AWS, GCP, or Azure), and API integration is typically required. Strong problem-solving skills, collaboration, and effective communication set standout engineers apart in this role. These competencies are crucial for building scalable, high-performance search applications and ensuring seamless integration with organizational data systems.

What is the difference between Pinecone vs Data Scientist?

AspectPineconeData Scientist
Required CredentialsTechnical skills in databases, APIs, and cloud platformsDegree in Computer Science, Statistics, or related fields; often includes certifications
Work EnvironmentTech companies, startups, cloud service providersResearch labs, corporate teams, consulting firms
Industry UsageData management, vector similarity search, AI applicationsData analysis, predictive modeling, machine learning
Common Search/ComparisonYesYes

While Pinecone specializes in vector database management and similarity search technology, Data Scientists focus on analyzing data, building models, and deriving insights. Both roles often collaborate in AI projects, but their core skills and tools differ significantly.

More about Pinecone jobs

What cities are hiring for Pinecone jobs?

Cities with the most 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 Pinecone jobs?

States with the most job openings for Pinecone jobs include:

Infographic showing various Pinecone job openings in the United States as of August 2026, with employment types broken down into 93% Full Time, and 7% Contract. Highlights an 71% Physical, 8% Hybrid, and 21% Remote job distribution.

ONLY W2 | AI Engineer@ Denver, Co

Palnar

Denver, CO • On-site

Full-time

Re-posted 10 hours ago


Job description

Job Summary,
  • We are looking for highly skilled AI Engineers with expertise in Large Language Models (LLMs) and hands-on experience with advanced AI platforms such as Gemini 2.5, GitHub Copilot, Glean, and other generative AI tools. The ideal candidate will be able to design, develop, and deploy AI-powered solutions that enhance productivity, accelerate workflows, and drive innovation.

Required Skills & Experience
  • Proven hands-on experience with Gemini 2.5, GitHub Copilot, Glean, or similar LLM-based tools.
  • Solid understanding of LLM architectures, embeddings, and retrieval-augmented generation (RAG).
  • Proficiency in Python, JavaScript/TypeScript, or similar programming languages.
  • Experience with cloud platforms (AWS, Azure, GCP) and AI/ML deployments.
  • Familiarity with Vector Databases (Pinecone, Weaviate, FAISS, Milvus, etc.).
  • Knowledge of ML pipelines, APIs, and prompt engineering.
Excellent problem-solving, communication, and collaboration skills.