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Vector Databases Jobs in McKinney, TX (NOW HIRING)

Photon

Data Engineer - Dallas, TX

Dallas, TX · On-site

$113K - $136K/yr

Vector Database Management: Architect and optimize Vector Databases (e.g., Pinecone, Weaviate, Milvus, or Qdrant) to ensure high-speed, relevant similarity searches for agentic retrieval. * Chunking ...

Photon

Data Engineer - Dallas, TX

Dallas, TX

$113K - $136K/yr

Vector Database Management: Architect and optimize Vector Databases (e.g., Pinecone, Weaviate, Milvus, or Qdrant) to ensure high-speed, relevant similarity searches for agentic retrieval. * Chunking ...

Photon

Data Lead- Dallas, TX

Dallas, TX · On-site

Vector Database Management: Architect and optimize Vector Databases (e.g., Pinecone, Weaviate, Milvus, or Qdrant) to ensure high-speed, relevant similarity searches for agentic retrieval. * Chunking ...

Redolent, Inc.

Sr. Full Stack Engineer

Irving, TX · On-site

Experience working with relational , NoSQL , and vector databases . Preferred Qualifications * Experience with Azure Machine Learning, Azure Functions, Azure App Services, Azure DevOps, AWS Lambda ...

Photon

Data Lead- Dallas, TX

Dallas, TX

Vector Database Management: Architect and optimize Vector Databases (e.g., Pinecone, Weaviate, Milvus, or Qdrant) to ensure high-speed, relevant similarity searches for agentic retrieval. * Chunking ...

Delan Associates, Inc.

AI Lead Engineer

Dallas, TX · On-site +1

$101K - $133K/yr

Embeddings & Vector Databases * Fine-tuning LLMs * Python * TensorFlow * PyTorch * Scikit-learn * Data Preprocessing & Feature Engineering * Model Training, Validation & Evaluation * Statistics & ML ...

Delan Associates, Inc

AI Lead Engineer

Dallas, TX · On-site +1

$101K - $133K/yr

... Vector Databases Fine-tuning LLMs Python TensorFlow PyTorch Scikit-learn Data Preprocessing & Feature Engineering Model Training, Validation & Evaluation Statistics & ML Algorithms MLOps CI/CD ...

Delan Associates, Inc

AI Lead Engineer (Remote)

Dallas, TX · Remote

$104K - $138K/yr

Embeddings & Vector Databases * Fine-tuning LLMs * Python * TensorFlow * PyTorch * Scikit-learn * Data Preprocessing & Feature Engineering * Model Training, Validation & Evaluation * Statistics & ML ...

Photon

Python Developer Dallas, TX

Dallas, TX

$49.75 - $68.50/hr

Develop and maintain Retrieval-Augmented Generation (RAG) solutions leveraging enterprise knowledge bases and vector databases. Tool Integration: Create tools, connectors, and APIs that enable agents ...

Judge Group, Inc.

Back-End Developer - III

Irving, TX · On-site

Vector Databases: Proficiency in using vector databases and Retrieval-Augmented Generation (RAG) techniques to ground AI models with external data and prevent hallucinations. APIs and Integrations:

Photon

Python Developer Dallas, TX

Dallas, TX · On-site

$49.75 - $68.50/hr

Develop and maintain Retrieval-Augmented Generation (RAG) solutions leveraging enterprise knowledge bases and vector databases. Tool Integration: Create tools, connectors, and APIs that enable agents ...

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All JobsVector Databases JobsVector Databases Jobs in McKinney, TX

Vector Databases information

What are vector databases?

Vector databases are specialized databases designed to store, manage, and search high-dimensional vector data, which is commonly generated from machine learning models, such as embeddings from natural language processing or image recognition. They enable efficient similarity search operations, such as finding the most similar items to a given query vector, which is essential for applications like recommendation systems, semantic search, and AI-powered search engines. Unlike traditional databases that handle structured or unstructured data, vector databases are optimized for fast and scalable similarity searches on large datasets of vectors.

What are some common challenges faced when working with vector databases, and how can they be addressed?

Professionals working with vector databases often encounter challenges such as efficiently scaling to handle large datasets, ensuring low-latency similarity searches, and integrating the database with machine learning pipelines. To address these, teams typically implement distributed architectures, fine-tune indexing strategies, and collaborate closely with data engineers and machine learning specialists. Staying updated with the latest developments in vector database technologies and maintaining clear communication with cross-functional teams are also key to overcoming these challenges.

What are the key skills and qualifications needed to thrive as a vector database engineer, and why are they important?

Success as a Vector Database Engineer requires a strong background in computer science, database management, and experience with machine learning or AI-driven data systems. Familiarity with vector database platforms (such as Pinecone, Milvus, or Weaviate), cloud infrastructure, and proficiency in languages like Python are typically expected. Strong problem-solving skills, effective communication, and the ability to work cross-functionally help engineers stand out. These competencies are vital to efficiently design, deploy, and maintain scalable vector search solutions that power modern AI applications.

What is the difference between Vector Databases vs Data Engineers?

AspectVector DatabasesData Engineers
Required SkillsDatabase management, data modeling, query optimizationData pipeline development, ETL processes, programming
Work EnvironmentData storage systems, AI/ML projects, cloud platformsData infrastructure, cloud environments, big data tools
Industry UsageAI, machine learning, recommendation systemsData integration, analytics, data architecture

While Vector Databases focus on storing and querying high-dimensional vector data for AI applications, Data Engineers build and maintain data pipelines and infrastructure to support data analysis and machine learning workflows. Both roles are essential in data-driven industries but serve different functions within the data ecosystem.

What job categories do people searching Vector Databases jobs in McKinney, TX look for?

The top searched job categories for Vector Databases jobs in McKinney, TX are:

What cities near McKinney, TX are hiring for Vector Databases jobs?

Cities near McKinney, TX with the most Vector Databases job openings:

Vector Databases jobs near you
Infographic showing various Vector Databases job openings in McKinney, TX as of August 2026, with employment types broken down into 85% Full Time, 9% Part Time, and 6% Contract. Highlights an 83% Physical, 6% Hybrid, and 11% Remote job distribution.

Data Engineer - Dallas, TX

Photon

Dallas, TX • On-site

$113K - $136K/yr

Full-time, Contractor

Medical, Dental, Vision, Retirement, PTO

Re-posted 13 days ago

Job description


We are seeking a Data Engineer who will be responsible for the "Ingestion-to-Insight" pipeline that allows autonomous agents to access, search, and reason over vast amounts of proprietary and public data.
Your role is critical: you will design the RAG (Retrieval-Augmented Generation) architectures and data pipelines that ensure our agents have the right context at the right time to make accurate decisions.
Key Responsibilities
  • AI-Ready Data Pipelines: Design and implement scalable ETL/ELT pipelines that process both structured (SQL, logs) and unstructured (PDFs, emails, docs) data specifically for LLM consumption.
  • Vector Database Management: Architect and optimize Vector Databases (e.g., Pinecone, Weaviate, Milvus, or Qdrant) to ensure high-speed, relevant similarity searches for agentic retrieval.
  • Chunking & Embedding Strategies: Collaborate with AI Engineers to optimize data chunking strategies and embedding models to improve the "recall" and "precision" of the agent's knowledge retrieval.
  • Data Quality for AI: Develop automated "Data Cleaning" workflows to remove noise, PII (Personally Identifiable Information), and toxicity from training/context datasets.
  • Metadata Engineering: Enrich raw data with advanced metadata tagging to help agents filter and prioritize information during multi-step reasoning tasks.
  • Real-time Data Streaming: Build low-latency data streams (using Kafka or Flink) to provide agents with "fresh" data, enabling them to act on real-time market or operational changes.
  • Evaluation Frameworks: Construct "Gold Datasets" and versioned data snapshots to help the team benchmark agent performance over time.

Required Skills & Qualifications
  • Experience: 4+ years in Data Engineering, with at least 1 year focusing on data for LLMs or AI/ML applications.
  • Python Mastery: Deep expertise in Python (Pandas, Pydantic, FastAPI) for data manipulation and API integration.
  • Data Tooling: Strong experience with modern data stack tools (e.g., dbt, Airflow, Dagster, Snowflake, or Databricks).
  • Vector Expertise: Hands-on experience with at least one major Vector Database and knowledge of similarity search algorithms (HNSW, Cosine Similarity).
  • Search Knowledge: Familiarity with hybrid search techniques (combining semantic search with traditional keyword search like Elasticsearch/BM25).
  • Cloud Infrastructure: Proficiency in managing data workloads on AWS, Azure, or GCP.

Preferred Qualifications
  • Experience with LlamaIndex or LangChain for data ingestion.
  • Knowledge of Graph Databases (e.g., Neo4j) to help agents understand complex relationships between data points.
  • Familiarity with "Data-Centric AI" principles-prioritizing data quality over model size.

Compensation, Benefits and Duration
Minimum Compensation: USD 38,000
Maximum Compensation: USD 133,000
Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.
Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full time employees.
This position is not available for independent contractors
No applications will be considered if received more than 120 days after the date of this post

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