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Pinecone Vector Databases Jobs (NOW HIRING)

ITBMS Inc.

AI/ML Engineer with Agentic AI

Plano, TX · On-site

Retrieval-Augmented Generation (RAG) : • Work with embedding models, vector databases, context windows, and chunking strategies • Work experience in Vector database (FAISS, Milvus, Pinecone or ...

Virtusa

Architect (ATC)

Tampa, FL

... vector databases Pinecone or Chroma or FAISS Ability to quickly conduct experiments and analyze the features and capabilities of newer versions of the LLM models as they come into market Basic data ...

Virtusa

Architect (ATC)

Tampa, FL · On-site

... vector databases Pinecone or Chroma or FAISS Ability to quickly conduct experiments and analyze the features and capabilities of newer versions of the LLM models as they come into market Basic data ...

Merican

AWS AI Engineer

Herndon, VA · On-site

Manage and optimize vector databases (e.g., Pinecone, Weaviate, Milvus) * Design and optimize Retrieval-Augmented Generation (RAG) pipelines for performance and scalability * Implement AI governance ...

Dminds Solutions Inc.

Data Analyst

Phoenixville, PA · On-site

Familiarity with vector databases (e.g., Pinecone, Milvus) and frameworks like Hugging Face or LangChain. * Expertise in ETL/ELT pipeline design and data warehousing * Bachelors in Computer Science ...

Virtusa

Architect (ATC)

Tampa, FL

... vector databases Pinecone or Chroma or FAISS Ability to quickly conduct experiments and analyze the features and capabilities of newer versions of the LLM models as they come into market Basic data ...

Virtusa

Architect (ATC)

Tampa, FL · On-site

... vector databases Pinecone or Chroma or FAISS Ability to quickly conduct experiments and analyze the features and capabilities of newer versions of the LLM models as they come into market Basic data ...

smart folks inc

IAM FullStack developer

Austin, TX · On-site

Experience with vector databases (FAISS/Milvus/Pinecone/pgvector) and document processing (PDF/HTML/markdown, chunking strategies). * Solid understanding of API security (OAuth2/OIDC/JWT), networking ...

Cna

Senior Data AI Engineer

Chicago, IL

$109K - $148K/yr

Proven experience designing and implementing vector databases (e.g., Vertex AI Vector Search, Pinecone, pgvector), embedding pipelines, and knowledge graph structures that underpin RAG and semantic ...

Vytwo

GenAI & Agentic AI Engineer

Prosper, TX · On-site

Work with vector databases (FAISS, Pinecone, Chroma, Weaviate) for semantic search. * Monitor, evaluate, and optimize GenAI models for accuracy, performance, and cost. Expertise You'll Bring: * 5+ ...

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Pinecone Vector Databases information

What is a Pinecone Vector Database?

A Pinecone Vector Database is a cloud-based service designed to efficiently store, index, and search high-dimensional vector data, such as embeddings generated by machine learning models. It enables fast similarity search, making it ideal for use cases like semantic search, recommendation systems, and AI-powered applications. Pinecone handles the complexity of scaling and managing vector data, so developers can focus on building intelligent applications without worrying about infrastructure.

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

To thrive as a Pinecone Vector Database Engineer, you need a strong background in computer science, data engineering, and experience with large-scale distributed systems, often supported by a relevant degree or equivalent experience. Proficiency in Python, REST APIs, cloud platforms (AWS, GCP), and vector search technologies, along with familiarity with Pinecone’s SDK and database management, are commonly required. Strong analytical thinking, problem-solving abilities, and effective communication skills help you collaborate with cross-functional teams and deliver scalable solutions. These skills ensure robust database performance, efficient data retrieval, and successful integration of vector search capabilities into real-world applications.

What are some common challenges faced by engineers working with Pinecone Vector Databases, and how can they be addressed?

Engineers working with Pinecone Vector Databases often encounter challenges such as optimizing vector search performance at scale, ensuring data consistency across distributed systems, and integrating the database with various machine learning pipelines. Addressing these challenges typically involves tuning indexing parameters, monitoring resource utilization, and collaborating closely with data scientists to understand retrieval requirements. Regularly reviewing documentation and participating in community forums can also help engineers stay current with best practices and new features.

What is the difference between Pinecone Vector Databases vs Data Engineers?

AspectPinecone Vector DatabasesData Engineers
Primary RoleManaging and deploying vector database solutions for AI/ML applicationsDesigning, building, and maintaining data pipelines and infrastructure
Skills & CertificationsKnowledge of vector databases, cloud platforms, programming (Python, SQL)Data modeling, ETL processes, cloud services, programming (Python, Java)
Work EnvironmentTech companies, AI startups, cloud providersData-driven organizations, tech firms, finance, healthcare

While Pinecone Vector Databases specialists focus on deploying and managing vector database solutions for AI applications, Data Engineers build and maintain the data infrastructure that supports these systems. Both roles require programming skills and familiarity with cloud platforms, but their core responsibilities differ: one centers on database management, the other on data pipeline development.

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Pinecone Vector Databases jobs near you
Infographic showing various Pinecone Vector Databases job openings in the United States as of July 2026, with employment types broken down into 91% Full Time, 5% Part Time, 1% Temporary, and 3% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution.
AI ML engineer with Agentic AI experience - Onsite

AI ML engineer with Agentic AI experience - Onsite

Emergere Technologies

Plano, TX • On-site

Other

Posted 5 days ago

Job description

Position: AI ML engineer with Agentic AI experience

Location: Plano, TX

Type: Contract

Job Profile:

An expert Prompt engineer with a strong software engineering a background and an excellent communicator with 8+ years of experience implementing AI and ML use cases. (Primarily AI)

Knowledge of Computer vision related projects.

LLM Expertise:

  • Must have hands-on experience working with modern LLMs (OpenAI, Anthropic, LLaMA, Mistral, Gemini),as well as a strong understanding of tokenization, model behaviors, reasoning patterns, and evaluation frameworks.
    • Prompt Design & Optimization (zero-shot, few-shot, chain-of-thought, ReAct, self-consistency)
    • Structured prompt templates
    • Refinement (prompt chaining, decomposition, and verification strategies)
    • Safety, Guardrails & Compliance
  • Experience building conversational flows (chatbots).

Retrieval-Augmented Generation (RAG) :

  • Work with embedding models, vector databases, context windows, and chunking strategies
  • Work experience in Vector database (FAISS, Milvus, Pinecone or any)

Machine Learning:

  • Machine Learning Engineer with strong experience in building, deploying, and optimizing end-to-end ML systems.
  • Skilled in data preprocessing, feature engineering, model development, and production deployment using modern ML frameworks.
  • Proficient in Python, PyTorch/ TensorFlow, cloud services, and MLOps practices.

Software Engineering:

  • Python proficiency.
  • Very good understanding and work experience with REST APIs, JSON, YAML.
  • Ability to integrate LLM prompts into production applications.
  • Familiarity with Git, version control, and experiment tracking.

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