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Pinecone Vector Databases Jobs (NOW HIRING)

Conflux Systems

AI Engineer with Python

Texas City, TX · On-site

Experience with vector databases such as Pinecone, Chroma, FAISS, or Milvus. * Familiarity with MLOps practices and model deployment pipelines. * Experience building conversational AI, AI assistants ...

New

ClifyX

Java Full stack Engineer

Irving, TX · On-site

Architect and optimize production-grade Retrieval-Augmented Generation (RAG) pipelines, focusing on advanced embedding strategies, efficient vector database management (Pinecone, Milvus, Chroma), and ...

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 ...

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+ ...

VeridianTech

$140K - $145K/yr

Work with vector databases (FAISS, Pinecone, Chroma, Weaviate) for semantic search. Monitor, evaluate, and optimize GenAI models for accuracy, performance, and cost. Required Skills & Experience 5+ ...

Symhas

Gen AI Lead - LangChain Specialist

Pleasanton, CA · On-site

$90 - $120/hr

Familiarity with vector databases (Pinecone, Weaviate, Chroma, etc.) * Experience with agentic patterns, tool calling, and memory management in LangChain * US Green Card or Citizenship required

AEG

Product Developer- CH- AXS

Charlotte, NC · On-site

... vector databases (e.g., Pinecone, Weaviate, Upstash Vector) • Experience with edge computing or serverless platforms • Background in developer platforms or API-first products • Knowledge of ML ...

Texas State Library and Archives Commision

AI Engineer

$107K - $146K/yr

LangChain LlamaIndex Semantic Kernel Experience working with vector databases Pinecone Weaviate Chroma FAISS Experience building REST APIs / GraphQL APIs Experience with cloud platforms Amazon Web ...

eTeam

Banking AI Engineer

Tampa, FL · On-site

... vector databases FAISS Chroma Pinecone or similar technologies • Ability to design and implement endtoend ML pipelines training validation deployment • Strong understanding of machine learning ...

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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.

Senior Technology Architect | Artificial Intelligence | Artificial Intelligence - ALL

Spruce Infotech

Irving, TX • On-site

$62.50 - $83.50/hr

Full-time

Posted 23 days ago

Job description

Job Title: GEN AI engineer & Agentic AI Engineer
Work Location : Dallas, or Charlotte Infosys
Vendor Rate: XXX/hour
Contract duration: 6-12 months depending performance
Target Start Date: 01-July 2026
Does this position require Visa independent candidates only? YES
Interview Process (Is face to face required?) Yes
Mandatory (Either in Dallas or in Charlotte at Client office)
Job Details:
Must Have Skills
GEN AI, Agentic AI Cortex AI,, ML Ops,Python, ML, Data Science, RAG,LLM
Nice to have skills
GCP, Prompt Engineering
We are seeking a highly skilled Generative AI Engineer with a strong Python background to design, develop, and deploy cutting-edge AI solutions. The ideal candidate will have hands-on experience with Large Language Models (LLMs), prompt engineering, and Gen AI frameworks, along with expertise in building scalable AI applications. Experience in Developing Agentic AI solutions.
Key Responsibilities:
Design and implement Generative AI models for text, image, or multimodal applications.
Develop prompt engineering strategies and embedding-based retrieval systems.
Integrate Gen AI capabilities into web applications and enterprise workflows.
Build agentic AI applications with context engineering and MCP tools. Required Skills & Qualifications:
10+ years of hands-on experience in AI, Data science, ML, GEN AI.
Strong hands on experience designing and deploying Retrieval-Augmented Generation (RAG) pipelines
Strong hands-on experience with RAG pipelines and vector databases
Extensive experience with LangChain, LangGraph, CrewAI, multi-agent orchestration
Strong MLOps / LLMOps experience with CI/CD automation
Experience across AWS (SageMaker, Lambda, EKS, S3) and GCP (Vertex AI)
API & microservices development using FastAPI, REST, Docker, Kubernetes
• Strong Python proficiency with PyTorch / TensorFlow
Strong MLOps/LLMOps experience with CI/CD automation,
Extensive experience with LangChain, LangGraph, and agentic AI patterns including routing, memory, multi-agent orchestration, guardrails, and failure recovery.
Experience in Developing microservices and API development using FastAPI, REST APIs, Pydantic/JSON schemas, Docker, and Kubernetes for low-latency serving.
Strong Hands-on experience with vector databases and semantic search technologies including Pinecone, FAISS, ChromaDB, and embedding lifecycle management
Strong proficiency in Python and AI/ML frameworks (PyTorch, TensorFlow).
Hands on experience using session and memory for building multi-agent systems along with using MCP tools.
Hands-on experience with LLMs, transformers, and Hugging Face ecosystem.
Knowledge and experience with vector databases and RAG technique for semantic search.
Familiarity with cloud AI services (AWS SageMaker, Azure OpenAI, GCP Vertex AI).
Understanding of MLOps practices for scalable AI deployment.
Strong experience in working with LLM fine-tuning with LoRA, QLoRA, PEFT,
Strong experience in Architected advanced RAG systems using Pinecone, FAISS, Weaviate, Chroma, hybrid retrieval, and custom embeddings,
Strong experience in Designing end-to-end LLMOps/MLOps pipelines using MLflow, DVC, SageMaker Pipelines, Vertex AI Pipelines, and GitHub Actions
Experience in using cloud-native AI systems on AWS (SageMaker, Lambda, EKS, EC2, Step Functions, S3, Glue) and GCP Vertex AI, supporting high-volume inference and secure enterprise operations
Experience in developing multi-agent orchestration workflows using LangGraph and CrewAI for tool-calling, validation agents, automated reasoning, and workflow supervision
Minimum years of experience
>10 years
Certifications Needed :NA
Top 3 responsibilities you would expect the Subcon to shoulder and execute
Strong experience in Developing Agents, MCP, Tools, GEN AI, LLM, RAG,ML, DL, Agentic AI ML Ops, LLMOps, Cloud platform,Model servicing optimization, Python
Strong communication skills
Strong programming skills
Interview Process (Is face to face required?) Yes
Mandatory (Either in Dallas or in Charlotte at Client office)
Any additional information you would like to share about the project specs/ nature of work
na
Project Code: WF GEN AI PLATFORM SUPPORT CTO

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