... · Vector databases & embeddings (Azure) · LLM evaluation, latency optimization, cost management, hallucination mitigation · AI governance, PII handling, security, and enterprise compliance ...
... · Vector databases & embeddings (Azure) · LLM evaluation, latency optimization, cost management, hallucination mitigation · AI governance, PII handling, security, and enterprise compliance ...
Sr. AI/ML Engineer (Airline exp must)- Fort Worth, TX (Hybrid) - F2F Interview
Fort Worth, TX · On-site
$100K - $137K/yr
Production experience building RAG systems, including vector databases (Pinecone, Weaviate, pgvector, FAISS) * Embedding models, chunking strategies, and retrieval optimization; experience with ...
Sr. AI/ML Engineer (Airline exp must)- Fort Worth, TX (Hybrid) - F2F Interview
Fort Worth, TX · On-site
$100K - $137K/yr
Production experience building RAG systems, including vector databases (Pinecone, Weaviate, pgvector, FAISS) * Embedding models, chunking strategies, and retrieval optimization; experience with ...
Sr AI Engineer
Fort Worth, TX · On-site +1
$80/hr
Integrate vector databases such as Azure AI Search and Amazon OpenSearch to support enterprise RAG systems. Evaluation & Quality Assurance * Define and run evaluation frameworks to measure answer ...
Sr AI Engineer
Fort Worth, TX · On-site +1
$80/hr
Integrate vector databases such as Azure AI Search and Amazon OpenSearch to support enterprise RAG systems. Evaluation & Quality Assurance * Define and run evaluation frameworks to measure answer ...
AI Architect
Fort Worth, TX · On-site
Integrate vector databases (e.g., Pinecone, FAISS, Milvus, Azure AI Search, Amazon OpenSearch). * Ensure relevance, freshness, observability, and security of RAG-based AI systems. Agentic AI ...
AI Architect
Fort Worth, TX · On-site
Integrate vector databases (e.g., Pinecone, FAISS, Milvus, Azure AI Search, Amazon OpenSearch). * Ensure relevance, freshness, observability, and security of RAG-based AI systems. Agentic AI ...
Familiar with RAG (Retrieval-Augmented Generation) pipelines, vector databases, or orchestration frameworks. * Capable of working with GraphQL, microservices, or event-driven systems in fast-paced ...
Familiar with RAG (Retrieval-Augmented Generation) pipelines, vector databases, or orchestration frameworks. * Capable of working with GraphQL, microservices, or event-driven systems in fast-paced ...
MLOps pipelines Model monitoring & observability Deployment to cloud or edge environments Vector databases & embeddings Retrieval-augmented pipelines Experience with distributed systems, data ...
MLOps pipelines Model monitoring & observability Deployment to cloud or edge environments Vector databases & embeddings Retrieval-augmented pipelines Experience with distributed systems, data ...
ERP AI Engineer - Manager
Fort Worth, TX · On-site
$99K - $232K/yr
... with vector databases and semantic search architectures - Translating complex business problems into AI solution designs - Contributing to business development and proposal writing - Cloud ...
ERP AI Engineer - Manager
Fort Worth, TX · On-site
$99K - $232K/yr
... with vector databases and semantic search architectures - Translating complex business problems into AI solution designs - Contributing to business development and proposal writing - Cloud ...
Experience with vector databases (e.g., Milvus, Pinecone, Chroma, FAISS or similar) and embedding-based retrieval. * Experience with GenAI model build: training, fine-tuning, and validation ...
Experience with vector databases (e.g., Milvus, Pinecone, Chroma, FAISS or similar) and embedding-based retrieval. * Experience with GenAI model build: training, fine-tuning, and validation ...
Experience with vector databases (e.g., Milvus, Pinecone, Chroma, FAISS or similar) and embedding-based retrieval. Experience with GenAI model build: training, fine-tuning, and validation; practical ...
Experience with vector databases (e.g., Milvus, Pinecone, Chroma, FAISS or similar) and embedding-based retrieval. Experience with GenAI model build: training, fine-tuning, and validation; practical ...
Required: * 6+ years of relevant analytics consulting or industry experience * 4+ years of experience with AI development tools such as vector databases (Pinecone, Elastic, etc.) and AI development ...
Required: * 6+ years of relevant analytics consulting or industry experience * 4+ years of experience with AI development tools such as vector databases (Pinecone, Elastic, etc.) and AI development ...
... vector databases, LangChain, or CrewAI * 1+ years of experience using Python, Structured Query Language (SQL), R, or SAS to prepare data for analysis, engineer features, visualize data, or support ...
... vector databases, LangChain, or CrewAI * 1+ years of experience using Python, Structured Query Language (SQL), R, or SAS to prepare data for analysis, engineer features, visualize data, or support ...
CTIO AI Engineering Manager
Fort Worth, TX · On-site
$73K - $244K/yr
... vector databases and orchestration tools like LangChain - Translating complex business problems into software-engineered AI solutions - Deploying on cloud platforms like AWS, GCP, Azure ...
CTIO AI Engineering Manager
Fort Worth, TX · On-site
$73K - $244K/yr
... vector databases and orchestration tools like LangChain - Translating complex business problems into software-engineered AI solutions - Deploying on cloud platforms like AWS, GCP, Azure ...
Senior Engineer - LLMOps & MLOps
Fort Worth, TX · On-site +1
$100K - $137K/yr
Design and execute the infrastructure for Retrieval-Augmented Generation (RAG), including vector database management (OpenSearch, Pinecone, or Azure AI Search) and semantic index optimization. • ...
New
Senior Engineer - LLMOps & MLOps
Fort Worth, TX · On-site +1
$100K - $137K/yr
Design and execute the infrastructure for Retrieval-Augmented Generation (RAG), including vector database management (OpenSearch, Pinecone, or Azure AI Search) and semantic index optimization. • ...
New
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 is the difference between Vector Databases vs Data Engineers?
| Aspect | Vector Databases | Data Engineers |
|---|---|---|
| Required Skills | Database management, data modeling, query optimization | Data pipeline development, ETL processes, programming |
| Work Environment | Data storage systems, AI/ML projects, cloud platforms | Data infrastructure, cloud environments, big data tools |
| Industry Usage | AI, machine learning, recommendation systems | Data 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 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 are popular job titles related to Vector Databases jobs in Cleburne, TX? For Vector Databases jobs in Cleburne, TX, the most frequently searched job titles are:
What job categories do people searching Vector Databases jobs in Cleburne, TX look for? The top searched job categories for Vector Databases jobs in Cleburne, TX are:
What cities near Cleburne, TX are hiring for Vector Databases jobs? Cities near Cleburne, TX with the most Vector Databases job openings:
Other
Posted 9 days ago
Job description
Job Title: Prompt Engineer
Location: Fort Worth, TX (In-person interview)
Duration: Long-term
Job Description:
Years of Experience Required: 10 years
Top 3 Mandatory Skills and Experience:
· Programming & Architecture
· Java, Python
· GenAI & LLM Engineering
· Hands-on development of GenAI / LLM-powered applications
· Prompt engineering, structured outputs, tool/function calling Agentic frameworks (LangChain, LangGraph, LangSmith)
· Retrieval Augmented Generation (RAG) and vector search
· Vector databases & embeddings (Azure)
· LLM evaluation, latency optimization, cost management, hallucination mitigation
· AI governance, PII handling, security, and enterprise compliance
Describe a great candidate that you are looking for and what skills and experience they will have:
· Ideal Candidate Profile – Senior GenAI & Contact Center Platform Engineer
· We are seeking a highly skilled, forward-thinking engineer who brings deep expertise across GenAI, cloud-native architecture, and contact center platforms, combined with the ability to thrive in a globally distributed, high-performing team spanning Hyderabad and DFW.
· Technical Excellence
· This candidate demonstrates strong proficiency in Java, Python, and TypeScript, with a solid foundation in object-oriented design, SOLID principles, and clean architecture. They have a proven track record designing and building scalable, distributed, event-driven systems that operate reliably in cloud-native environments.
· They are hands-on with backend development (Spring Boot, FastAPI) and experienced in building robust APIs (REST/GraphQL) and event-driven integrations using Kafka. Their data layer experience spans relational and NoSQL systems, including PostgreSQL, MongoDB, Redis, and CosmosDB.
· GenAI & LLM Engineering Leadership