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Pinecone Vector Databases Jobs (NOW HIRING)

CCS INC

Lead Gen AI Engineer

Plano, TX · On-site

$85 - $110/hr

Deep understanding of LLMs, embeddings, vector databases (e.g., FAISS, Pinecone, Weaviate). * Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes)

Precision Technologies Corp

Sr. Data/GenAI Engineer

Irving, TX · On-site

$101K - $138K/yr

... vector databases (e.g., Pinecone, Chroma, PGvector) and the concept of embeddings. o Experience working with a variety of data sources, from structured databases to semi-structured API outputs. o ...

OmegaHires

Lead Gen AI Engineer

Phoenix, AZ · On-site

$101K - $134K/yr

... Vector Databases such as Pinecone, FAISS, ChromaDB, Weaviate, or Milvus. - Experience integrating LLMs including OpenAI, Azure OpenAI, Gemini, Claude, Llama, or Mistral. - Strong knowledge of Prompt ...

New

Inficare Technologies

Lead AI Engineer

$104K - $138K/yr

Vector databases: Pinecone, Weaviate, ChromaDB * JavaScript / TypeScript for AI frontend integration * Git, Docker, Kubernetes * SQL / NoSQL databases for AI data management * MLOps tools and ...

Cliff Services Inc

AI/LLM Eng

Jersey City, NJ · On-site

... Vector databases (pgvector, Pinecone, Chroma, etc.) Python backend development (FastAPI/Flask) API integrations and workflow orchestration Deep Learning & Machine Learning (model training, fine ...

INFOSYS NOVA HOLDINGS LLC

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 ...

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 ...

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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 August 2026, with employment types broken down into 87% Full Time, 5% Part Time, 1% Temporary, and 7% Contract. Highlights an 83% Physical, 6% Hybrid, and 11% Remote job distribution.

Lead Gen AI Engineer

CCS INC

Plano, TX • On-site

$85 - $110/hr

Full-time

Medical, Dental, Vision

Re-posted 14 days ago

Job description

Benefits:
  • Bonus based on performance
  • Dental insurance
  • Health insurance
  • Vision insurance

 
Qualifications:
 
  • 8+ years of software engineering and development experience
  • Proven experience in building and deploying GenAI applications in production.
  • Strong programming skills in Python and familiarity with GenAI libraries (Transformers, LangChain, Hugging Face, etc.).
  • Deep understanding of LLMs, embeddings, vector databases (e.g., FAISS, Pinecone, Weaviate).
  • Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
  • Familiarity with CI/CD for ML workflows and versioning tools like MLflow or DVC.
  • Hands-on experience designing and building cloud-native solutions (preferably on AWS)
  • Exposure to GenAI tools and frameworks (e.g., LLMs, vector databases, prompt orchestration, LangChain, Bedrock)
  • Familiarity with AWS AI/ML services (e.g., SageMaker, Bedrock, Comprehend, Lex)
  • AWS AI certification
 
 
Responsibilities
  • Design scalable and robust GenAI architectures using LLMs, multimodal models, and retrieval-augmented generation (RAG).
  • Fine-tune foundation models using domain-specific data.
  • Implement prompt engineering, instruction tuning, and reinforcement learning from human feedback (RLHF).
  • Integrate GenAI capabilities into enterprise platforms using APIs, SDKs, and orchestration tools.
  • Implement responsible AI practices including bias detection, hallucination mitigation, and explainability.
  • Monitor and optimize model performance, latency, and cost.
  • Use techniques like quantization, distillation, and caching to improve efficiency.