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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 ...

CCS Corp

Gen AI Engineer

Plano, TX · On-site

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

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)

Guru Schools

Applied AI Engineer

Houston, TX · On-site

Embeddings & vector databases (e.g., FAISS, Pinecone, ChromaDB) * Prompt engineering and fine-tuning * LLM APIs (e.g., OpenAI, Claude, Gemini) * Experience deploying cloud-native solutions using GCP ...

Purple Drive Technologies

Gen AI architect

Mclean, VA · On-site

$63.75 - $84/hr

Experience with vector databases (e.g., Pinecone, Weaviate, or similar). * Experience deploying AI solutions in cloud environments (AWS, Azure, GCP). * Deep understanding of Responsible AI principles ...

CCS Corp

Lead Gen AI Engineer

Plano, TX · On-site

$98K - $129K/yr

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

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 ...

Purple Drive Technologies

Sr Python Developer

Tampa, FL · On-site

$114K - $154K/yr

Integrate with external data sources (databases, APIs, vector databases like Pinecone, Weaviate, or FAISS) for context-rich AI solutions. * Collaborate with data scientists, ML engineers, and product ...

Guru Schools

Applied AI Engineer

Houston, TX · On-site

Embeddings & vector databases (e.g., FAISS, Pinecone, ChromaDB) * Prompt engineering and fine-tuning * LLM APIs (e.g., OpenAI, Claude, Gemini) * Experience deploying cloud-native solutions using GCP ...

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 ...

Compunnel

Lead AI Engineer

Coppell, TX · On-site

$94K - $124K/yr

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

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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 May 2026, with employment types broken down into 96% Full Time, and 4% Part Time. Highlights an 69% Physical, 5% Hybrid, and 26% Remote job distribution.

AI AWS Technical Architect

Purple Drive Technologies

Parsippany, NJ • On-site

$65 - $85.50/hr

Full-time

Posted 11 days ago

Job description

Overview:
Job Title: AI AWS Technical Architect
Experience: 6-8 Years
Job Type: Full-Time
Job Summary
We are seeking an experienced AI AWS Technical Architect with strong expertise in designing and implementing enterprise-scale AI/ML and Generative AI solutions on AWS cloud platforms. The ideal candidate will possess hands-on experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), cloud-native AI architectures, MLOps, and scalable distributed systems.
This role requires strong technical leadership, architecture design capabilities, and hands-on engineering expertise to deliver secure, scalable, and high-performance AI-driven applications.
Required Skills
  • Strong expertise in:
    • AWS Cloud Architecture
    • Generative AI
    • Large Language Models (LLMs)
    • AI/ML Solution Design
  • Hands-on experience with:
    • Amazon Bedrock
    • SageMaker
    • AWS Lambda
    • Amazon EKS
    • API Gateway
  • Strong programming expertise in:
    • Python
    • Node.js
  • Experience with:
    • LangChain
    • AutoGen
    • LangGraph
    • Agentic AI frameworks
  • Strong understanding of:
    • RAG architectures
    • Embeddings
    • Vector databases
    • Prompt engineering
  • Experience with vector databases such as:
    • Qdrant
    • Pinecone
    • OpenSearch
    • MongoDB Atlas Vector Search
  • Expertise in:
    • CI/CD pipelines
    • MLOps
    • Kubernetes
    • Containerization
  • Strong understanding of:
    • Responsible AI
    • AI governance
    • Security best practices
    • FinOps optimization
Key Responsibilities
  • Design and architect scalable AI/ML and Generative AI solutions on AWS cloud
  • Build and implement RAG-based enterprise AI systems using LLMs and vector databases
  • Develop AI orchestration workflows and agentic AI solutions using modern AI frameworks
  • Architect scalable cloud-native AI infrastructure leveraging AWS services
  • Define embedding strategies, prompt engineering standards, and retrieval optimization techniques
  • Establish MLOps practices including:
    • CI/CD pipelines
    • Model deployment
    • Monitoring
    • Lifecycle management
  • Implement security, governance, and Responsible AI best practices
  • Optimize AI systems for:
    • Performance
    • Reliability
    • Cost efficiency
    • Scalability
  • Collaborate with engineering, DevOps, product, and business teams to drive AI initiatives
  • Mentor technical teams and provide architectural guidance across AI programs
Preferred Qualifications
  • Experience building enterprise AI platforms and distributed systems
  • AWS Certifications preferred:
    • AWS Solutions Architect
    • AWS Machine Learning Specialty
  • Exposure to Kubernetes and cloud-native deployment patterns
  • Strong communication and stakeholder management skills

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