ZipRecruiter

Log In

1

Pinecone Vector Databases Jobs (NOW HIRING)

Staffingine LLC

AI Engineer

Phoenix, AZ · On-site

Develop and implement AI solutions using Python and AI frameworks such as Langgraph and Langchain Work with vector databases like Pinecone to manage and query highdimensional data Build and maintain ...

Innowhyte Inc

Build and implement RAG pipelines with vector databases (e.g., Pinecone, FAISS). Develop Generative AI solutions, including chatbots, summarization, and content creation tools. Preprocess, clean, and ...

Search Solutions

Be Seen First

Manager of Data Engineering

Dallas, TX · Remote

$160K - $190K/yr

Design and optimize the storage of embeddings in Vector Databases (e.g., Pinecone, ChromaDB, Vertex AI Search) and Graph Databases (e.g., FalkorDB, Neo4j) to enable multi-step agentic reasoning ...

Tata Consultancy Services

Java GENAI Engineer

Phoenix, AZ · On-site

$51.50 - $70.50/hr

... vector databases • Experience with Spring AI / LangChain4j • Vector DBs (Pinecone, OpenSearch, pgvector) • Knowledge of AI agents / orchestration frameworks • Cloud AI platforms (Azure AI ...

CCS INC

Gen AI Engineer

Plano, TX · On-site

$40 - $50/hr

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

Purple Drive

AI AWS Technical Architect

Parsippany, NJ · On-site

$65 - $85.50/hr

Experience with vector databases such as: * Qdrant * Pinecone * OpenSearch * MongoDB Atlas Vector Search * Expertise in: * CI/CD pipelines * MLOps * Kubernetes * Containerization * Strong ...

Prophecy Technologies

AI ML Engineer

Sunrise, FL · On-site

Develop and optimize RAG pipelines using vector databases (FAISS, Pinecone, Chroma) * Deploy models via APIs, microservices, Docker, and cloud platforms (AWS / GCP / Azure) * Implement CI/CD ...

CCS INC

Lead Gen AI Engineer

Plano, TX

$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 ...

TMS

AI/ ML Engineer

New York, NY · Remote

$60 - $62/hr

Knowledge of vector databases (Pinecone, FAISS, Weaviate, ChromaDB) * Experience with REST APIs and microservices * Familiarity with cloud platforms (AWS, Azure, or GCP) Good to Have * Experience ...

CipherMax LLC

Senior Java Developer

Seattle, WA · On-site

$65.25 - $83/hr

Vector Databases (Pinecone, ChromaDB, FAISS, OpenSearch) * AWS and/or Google Cloud Platform (Google Cloud Platform) * Docker, Kubernetes, CI/CD * Strong experience building enterprise-scale AI ...

Compunnel

Senior AI Engineer

Coppell, TX · On-site

$96K - $132K/yr

... vector databases such as PGVector, Vertex Matching Engine, and Pinecone. • Familiarity with Kafka or Pub/Sub eventing patterns. • Implement and maintain observability stacks: OpenTelemetry ...

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 ...

Technology Ventures

Senior GenAI Engineer

Reston, VA · On-site

$108K - $149K/yr

Implement Retrieval Augmented Generation (RAG) solutions using vector databases (pgvector, Pinecone, Weaviate). Perform data analysis, preparation, and curation to build high-quality datasets for AI ...

next page

Showing results 1-20

All JobsPinecone Vector Databases Jobs

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.

More about Pinecone Vector Databases jobs
What cities are hiring for Pinecone Vector Databases jobs? Cities with the most Pinecone Vector Databases job openings:
What states have the most Pinecone Vector Databases jobs? States with the most job openings for Pinecone Vector Databases jobs include:
What job categories do people searching Pinecone Vector Databases jobs look for? The top searched job categories for Pinecone Vector Databases jobs are:
Pinecone Vector Databases jobs near you
AI Engineer

AI Engineer

Staffingine LLC

Phoenix, AZ • On-site

Contractor

Posted yesterday

Job description

Job Title: AI Engineer
Job Location: Phoenix, AZ
Job Type: Contract

Job Description:

  1. Develop and implement AI solutions using Python and AI frameworks such as Langgraph and Langchain Work with vector databases like Pinecone to manage and query highdimensional data Build and maintain machine learning pipelines for data processing and model deployment Utilize ML frameworks including XGBoost and PyTorch for model development and training Design and optimize data processing workflows using SQL and other pipeline tools Integrate and manage APIs to support AI applications and services Collaborate with crossfunctional teams to deliver scalable AIdriven products 

Roles and Responsibilities 

  1. Design develop and deploy AI and machine learning models using Python and relevant AI stacks Manage vector databases to enhance data retrieval and storage efficiency Build robust ML pipelines to automate data ingestion preprocessing and model training Apply advanced ML frameworks such as XGBoost and PyTorch for predictive analytics Develop data processing solutions involving SQL and pipeline orchestration Create and maintain APIs for seamless integration of AI components Collaborate with data scientists engineers and stakeholders to ensure project success Continuously monitor and improve AI system performance and scalability

Skills

Mandatory Skills : Agentic Framework