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Vector Databases Jobs in Georgetown, TX (NOW HIRING)

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 ...

Vkore Solutions

GenAI Engineer-W2

Austin, TX · On-site

Manage datasets, preprocess data, and implement RAG with vector databases (FAISS, Chroma, Pinecone). * Automate training workflows using ML flow, Weights & Biases, and Ray. * Deploy models using ...

Zilliz

Enterprise Account Executive - Texas

Austin, TX · Remote

Zilliz is a fast-growing startup developing the industry's leading vector database company for enterprise-grade AI. Founded by the engineers behind Milvus, the world's most popular open-source vector ...

Zilliz

Enterprise Account Executive - Texas

Austin, TX · On-site

$100K - $175K/yr

Zilliz is a fast-growing startup developing the industry's leading vector database company for enterprise-grade AI. Founded by the engineers behind Milvus, the world's most popular open-source vector ...

Quest Global

AI/ML Engineer

Austin, TX · On-site

They should have knowledge of DE. and some knowledge in AWS or some cloud. • Assess and integrate open-source tools, orchestration frameworks, context management solutions, and vector databases ...

Emerson Group

Senior AI Software Engineer

Austin, TX · On-site

$121K - $160K/yr

Understanding of RAG architectures, vector databases, embeddings, and semantic search concepts. * Proven ability to rapidly prototype solutions, validate approaches, and iterate quickly. * Strong ...

Emerson

Senior AI Software Engineer

Austin, TX · On-site

$121K - $160K/yr

Understanding of RAG architectures, vector databases, embeddings, and semantic search concepts. * Proven ability to rapidly prototype solutions, validate approaches, and iterate quickly. * Strong ...

Tata Consultancy Service Limited

Java Engineer

Austin, TX · On-site

$110K - $125K/yr

Vector databases (Pinecone, Weaviate, pgvector) Roles & Responsibilities KEY RESPONSIBILITIES Technical Leadership * Drive architectural decisions and technical roadmap * Establish coding standards ...

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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?

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 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 Georgetown, TX? For Vector Databases jobs in Georgetown, TX, the most frequently searched job titles are:
What job categories do people searching Vector Databases jobs in Georgetown, TX look for? The top searched job categories for Vector Databases jobs in Georgetown, TX are:
What cities near Georgetown, TX are hiring for Vector Databases jobs? Cities near Georgetown, TX with the most Vector Databases job openings:
Vector Databases jobs near you
Database Engineer - RAG Platform Developer

Database Engineer - RAG Platform Developer

Apple

Austin, TX • On-site

Full-time

Posted yesterday

Apple rating

8.1

Company rating: 8.1 out of 10

Based on 661 frontline employees who took The Breakroom Quiz

6th of 30 rated technology retailers

Job description

We're seeking a Database Engineer to architect and optimize our large-scale RAG (Retrieval-Augmented Generation) platform that serves our users across all of the Hardware Tech group. This role combines deep database expertise with modern AI/ML infrastructure, enabling design teams to seamlessly onboard and query enterprise-scale datasets. You'll be responsible for database architecture and optimization while also contributing to full-stack GenAI application development.
As a Database Engineer on our team, you will architect and optimize our SQL and vector database infrastructure supporting enterprise-scale design data. You'll lead technical decisions on database architecture, scaling patterns, and technology selection for our RAG platform while designing comprehensive strategies to ensure optimal performance. Working closely with the development team, you'll build and refine data ingestion pipelines that enable design teams across all disciplines to seamlessly onboard their data. You'll collaborate with DevOps/SRE teams to ensure quality of service, proper resource allocation, and system scalability while improving RAG retrieval performance through hybrid search strategies, index tuning, and embedding optimization. In addition to your primary database focus, you'll contribute to full-stack development using Python and JavaScript, monitor database health and performance metrics for our multi-tenant system, and develop and maintain database operations procedures, monitoring, and disaster recovery strategies while driving continuous improvement of retrieval quality, search latency, and overall system reliability. You'll also provide mentorship to other engineers on database best practices and scalable design patterns.
Proficiency in Python or Javascript.Production experience deploying and managing vector databases (Milvus, Qdrant, or Weaviate) at scaleExperience with PostgreSQL or MySQL in production environmentsUnderstanding of RAG pipelines, including embedding strategies, chunking, and retrieval optimizationMinimum requirement of BS + 10 years of relevant industry experience
Understanding of Vector database indexing strategies and tradeoffsStrong SQL proficiency with deep understanding of query planning, indexing strategies, and optimization techniquesPostgres advanced features (extensions, replication, sharding)Experience managing large-scale databases serving high-concurrency workloadsExperience with embedding models and LLM integration patternsDemonstrated experience building or optimizing RAG systems in production environmentsCollaborative mindset with ability to mentor engineers and work closely with DevOps/SRE teamsMonitoring and observability tools (Prometheus, Grafana)Kubernetes experience, particularly with stateful applications and database deploymentsProven ability to make architectural decisions for scalable database systems

What Apple employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976

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