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Pinecone Vector Databases Jobs in Addison, TX (NOW HIRING)

Photon

Backend Lead - Python - Dallas, TX

Dallas, TX · On-site

Oversee the integration of Vector Databases (e.g., Pinecone, Weaviate, or PGVector) and design "memory" systems that allow agents to retain context across sessions. * Observability & Tracing:

INFOSYS NOVA HOLDINGS LLC

AI Engineer

Fort Worth, TX · On-site

$114K - $150K/yr

Build and manage vector database solutions using Pinecone, Weaviate, Milvus, FAISS, Chroma, OpenSearch Vector Engine, Azure AI Search, Vertex AI Vector Search, or pgvector. * Deploy and optimize LLM ...

Accenture

Knowledge Engineer - Back End

Dallas, TX · On-site

Minimum 3 years experience deploying and operating vector databases (e.g., Pinecone, Weaviate, Milvus, or Qdrant) in production environments. * Minimum 3 years Proficiency in Python or Java for ...

New

@Orchard

Senior Azure Generative AI Engineer

Dallas, TX · On-site

$95/hr

Experience with vector databases such as Azure AI Search, Pinecone, Weaviate, or similar technologies. * Strong Python programming skills. * Experience with LangChain, LangGraph, Semantic Kernel, or ...

RealPage, Inc.

AI Engineer (DFW Area)

Richardson, TX · On-site

Choose and configure vector databases (e.g., PGVector, Vertex AI Search, Pinecone, etc.) * Build ingestion pipelines for internal data (documents, tickets, logs, property data, etc.) * Implement ...

RealPage, Inc.

AI Engineer (DFW Area)

Richardson, TX · On-site

$107K - $182K/yr

Choose and configure vector databases (e.g., PGVector, Vertex AI Search, Pinecone, etc.) * Build ingestion pipelines for internal data (documents, tickets, logs, property data, etc.) * Implement ...

Confie

AI Engineer

Addison, TX · On-site +1

$110K - $140K/yr

Proficiency in Python, LangChain/LlamaIndex, and vector databases (Pinecone, Weaviate, Chroma, pgvector, Snowflake). Expertise in prompt engineering including chain-of-thought, few-shot learning, and ...

RealPage, Inc.

AI Engineer (DFW Area)

Richardson, TX · On-site

$107K - $182K/yr

Choose and configure vector databases (e.g., PGVector, Vertex AI Search, Pinecone, etc.) * Build ingestion pipelines for internal data (documents, tickets, logs, property data, etc.) * Implement ...

Winaxis

AI/ML Engineer

Dallas, TX · On-site

$113K - $136K/yr

LangChain LlamaIndex Hugging Face OpenAI APIs Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS) Experience in RAG (Retrieval-Augmented Generation) implementations. Knowledge of MLOps tools and ...

Winaxis

AI/ML Engineer

Dallas, TX · On-site

$113K - $136K/yr

LangChain LlamaIndex Hugging Face OpenAI APIs Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS) Experience in RAG (Retrieval-Augmented Generation) implementations. Knowledge of MLOps tools and ...

Winaxis

AI/ML Engineer

Dallas, TX · On-site

$113K - $136K/yr

LangChain LlamaIndex Hugging Face OpenAI APIs Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS) Experience in RAG (Retrieval-Augmented Generation) implementations. Knowledge of MLOps tools and ...

Winaxis

AI/ML Engineer

Dallas, TX · On-site

$113K - $136K/yr

LangChain LlamaIndex Hugging Face OpenAI APIs Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS) Experience in RAG (Retrieval-Augmented Generation) implementations. Knowledge of MLOps tools and ...

Winaxis

AI/ML Engineer

Dallas, TX · On-site

$113K - $136K/yr

LangChain LlamaIndex Hugging Face OpenAI APIs Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS) Experience in RAG (Retrieval-Augmented Generation) implementations. Knowledge of MLOps tools and ...

Winaxis

AI/ML Engineer

Dallas, TX · On-site

$113K - $136K/yr

LangChain LlamaIndex Hugging Face OpenAI APIs Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS) Experience in RAG (Retrieval-Augmented Generation) implementations. Knowledge of MLOps tools and ...

IT America Inc

Agentic AI Engineer Lead

Dallas, TX · On-site

$101K - $133K/yr

Implement knowledge graphs, vector databases (Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) techniques for enhanced agent reasoning. Apply reinforcement learning (RLHF/RLAIF ...

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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.

What job categories do people searching Pinecone Vector Databases jobs in Addison, TX look for? The top searched job categories for Pinecone Vector Databases jobs in Addison, TX are:
What cities near Addison, TX are hiring for Pinecone Vector Databases jobs? Cities near Addison, TX with the most Pinecone Vector Databases job openings:
Pinecone Vector Databases jobs near you
Infographic showing various Pinecone Vector Databases job openings in Addison, TX as of August 2026, with employment types broken down into 43% Full Time, and 57% Contract. Highlights an 71% In-person, and 29% Remote job distribution.

Senior Cloud Data & AI Architect

Ventures Unlimited Inc

Dallas, TX • On-site

$66.75 - $89.50/hr

Other

This job post has expired today. Applications are no longer accepted.

Job description

Locations: Los Angeles, CA / Dallas, TX / Chicago, IL

Must Have Technical/Functional Skills

• Architect enterprise data platforms for data lake, Lakehouse, streaming systems.

• Design data integration and data pipeline patterns

• Should be able to evaluate new technologies and run proof of concepts.

• Should be able to set data and AI strategy for data organization.

• Established data Quality, lineage and metadata standards

• Ensured compliance with privacy, security and regulation

• Drives adoption of responsible AI frameworks

• Created architectural guardrails

• Drive consensus on standards (eg data contracts, lineage) across different data organizations

• Reviews design and elevate architectural thinking across teams

• Creates reusable patterns, templates and reference architectures

• Very strong understanding and experience on Data products, data mesh and Medallion Architecture implementation

• Design and implement AI and Gen AI solution for data value chain

• Strong experience with LLMs, prompt engineering, and agent frameworks (LangChain, AutoGen, CrewAI).

• Deep understanding of MCPs, ReAct, Tree of Thought, and AutoGPT-style reasoning.

• Hands-on with Python, OpenAI APIs, Anthropic Claude, Vector DBs (FAISS, Pinecone, Weaviate).

• Experience with A2A orchestration, agent memory strategies, and tool calling.

• Strong grasp of enterprise architecture, data governance, and security protocols.

• Experience with cloud platforms (Azure, AWS, GCP) and MLOps pipelines.

• Very strong understanding and experience on Data products, data mesh and Medallion Architecture implementation

• Implement retrieval-augmented generation (RAG) pipelines with memory, context management, and tool usage.

• Define Model Context Protocol (MCPs) to chain reasoning, retrieval, and action models.

• Hands-on with Python, OpenAI APIs, Anthropic Claude, Vector DBs (FAISS, Pinecone, Weaviate).


Roles & Responsibilities

• Lead enterprise data platform modernization from on-prem to cloud for banking and insurance clients.

• Architect and implement scalable solutions using Snowflake and Databricks on AWS and Azure

Design and implement AI and Gen AI solution for data value chain

• Design data integration pipelines (batch, real-time, big data) and analytics platforms

• Define and implement data governance, quality, meta data, and lineage frameworks and should be able to leverage GenAI capabilities.

• Act as a trusted advisor to senior business and IT stakeholders

Architect Agentic AI ecosystems using LLMs, vector databases, and orchestration frameworks (LangChain, AutoGen, CrewAI).

• Define Model Context Protocol (MCPs) to chain reasoning, retrieval, and action models.

• Design Agent-to-Agent (A2A) communication protocols for collaborative multi-agent workflows.

• Implement retrieval-augmented generation (RAG) pipelines with memory, context management, and tool usage.


Generic Managerial Skills, If any

• 15–20 years of experience in data architecture, data engineering, and analytics platforms

• Strong consulting experience in large BFSI transformation programs

• Hands-on expertise with Snowflake and Databricks (Lakehouse architecture)

• Design and implement AI and Gen AI solution for data value chain

• Very strong understanding and experience on Data products, data mesh and Medallion Architecture implementation

• Experience with cloud data services in aws,azure,gcp

• Strong background in data integration, reporting, and big data ecosystems

• Experience working in regulated environments with data governance and compliance requirements

• Excellent stakeholder communication and leadership skills

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