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Vector Databases Jobs in Boca Raton, FL (NOW HIRING)

Leo Technologies

DevOpsEngineer

Wellington, FL

$49.25 - $67.50/hr

GPU clusters, model serving (vLLM, TGI, Triton, Ollama, TensorRT-LLM), vector databases (pgvector, Weaviate, Qdrant, Milvus), and graph databases (Neo4j, Neptune). * Operate Kubernetes (EKS, AKS, GKE ...

IntelePeer

Senior ML Engineer

Dania Beach, FL · On-site

$102K - $141K/yr

Build and maintain embedding pipelines -- generate, index, and retrieve dense embeddings using vector databases (Pinecone, pgvector, Weaviate, or similar) for RAG and semantic search applications.

Transunion

Staff Site Reliability Engineer

Boca Raton, FL

$54 - $72/hr

Hands-on experience designing and integrating AI/ML-powered solutions into cloud-native platforms - including familiarity with LLM orchestration, vector databases, model serving infrastructure, and ...

TransUnion

Staff Site Reliability Engineer

Boca Raton, FL

$54 - $72/hr

Hands-on experience designing and integrating AI/ML-powered solutions into cloud-native platforms - including familiarity with LLM orchestration, vector databases, model serving infrastructure, and ...

Trans Union LLC

Staff Site Reliability Engineer

Boca Raton, FL · On-site

$54 - $71.75/hr

Hands-on experience designing and integrating AI/ML-powered solutions into cloud-native platforms - including familiarity with LLM orchestration, vector databases, model serving infrastructure, and ...

Humana

Lead Solutions Architect

Fort Lauderdale, FL

Proven ability to integrate AI systems with event-driven microservices, vector databases/feature stores, and CI/CD pipelines, while leading reference architectures and cross-cloud patterns that scale ...

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All JobsVector Databases JobsVector Databases Jobs in Boca Raton, FL

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.
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Vector Databases jobs near you

Gen AI Engineer - Sunrise, FL - Contract Opportunity

Zodiac Solutions

Sunrise, FL • On-site

Contractor

Re-posted 17 days ago

Job description

Role: Gen AI Engineer

Location: Sunrise, FL (Onsite/Hybrid as per client requirement)
Duration: Long-Term Contract
Required Skills: Python, GenAI, LLM, LangChain, LangGraph, RAG.


Experience: 6+ Years

Job Description:

  • Design and develop AI-powered applications using Python and modern GenAI frameworks.
  • Build and optimize LLM-based solutions using LangChain and LangGraph.
  • Develop RAG pipelines by integrating vector databases and enterprise knowledge sources.
  • Experience with prompt engineering, RAG (Retrieval-Augmented Generation), and vector databases.
  • Knowledge of AI model integration using APIs such as OpenAI, Anthropic, or similar platforms.
  • Experience developing and deploying AI applications in cloud environments (AWS, Azure, or GCP).
  • Strong understanding of REST APIs, microservices, and scalable application architecture.
  • Familiarity with Git, CI/CD pipelines, and Agile methodologies.