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Vector Databases Jobs in Orlando, FL (NOW HIRING)

DRMP

Applied AI Field Engineer (Orlando)

Orlando, FL · On-site

... vector databases, and agent frameworks • Experiment with new tools and techniques to improve speed, quality, and capability • Contribute reusable patterns, components, and best practices across ...

247Hire

Machine Learning Engineer

Orlando, FL · On-site

$120 - $160/hr

Vector databases (Pinecone, Weaviate), feature stores, data versioning Specialized Tools Frameworks: Autogen, LangChain, MCP (Model Context Protocol) Evaluation: Custom metrics, human evaluation ...

Capco

Mid Level AI Engineer

Orlando, FL · Hybrid

Develop and optimize Retrieval-Augmented Generation (RAG) solutions leveraging vector databases and enterprise knowledge sources. * Create intelligent AI agents and workflows capable of interacting ...

Trilon

Applied AI Field Engineer

Orlando, FL · On-site

Stay current with advancements in LLMs, vector databases, and agent frameworks * Experiment with new tools and techniques to improve speed, quality, and capability * Contribute reusable patterns ...

Trilon Group

Applied AI Field Engineer

Orlando, FL · On-site

$155K - $190K/yr

Stay current with advancements in LLMs, vector databases, and agent frameworks * Experiment with new tools and techniques to improve speed, quality, and capability * Contribute reusable patterns ...

CYNET SYSTEMS

DOTNET AI Architect

Orlando, FL · On-site

$47 - $52/hr

Familiarity with vector databases and semantic search technologies. * Experience with prompt engineering, model orchestration frameworks, and AI agents. * Exposure to data engineering, analytics, and ...

Fortress

Senior Software Engineer, AI Platform

Orlando, FL · On-site

$140.54 - $223.21/hr

Experience with vector databases, graph databases, search platforms, and modern retrieval architectures involving embeddings, hybrid search, reranking, or knowledge graph traversal * Experience with ...

Tror AI for everyone

AI Engineer

Lake Mary, FL · On-site

Vector database - pgvector * Hybrid search (keyword (BM25) + semantic) * Re-ranking and relevance tuning. * Agentic Frameworks and Orchestration. * Multi-agent coordination * Memory Management

Disney Experiences

Sr Software Engineer

Orlando, FL

$135K - $181K/yr

Experience with RAG (Retrieval Augmented Generation), vector databases, and embeddings * Excellent programming skills in languages Java and Python. * Experience with cloud platforms (AWS, GCP, or ...

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All JobsVector Databases JobsVector Databases Jobs in Orlando, 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

Backend Python Developer - AI/ML

Snowrelic Inc

Orlando, FL • On-site

Full-time

Re-posted 19 days ago

Job description

Job : Backend Python Developer – AI/ML

Location : Orlando preferable, Las Vegas

Skills : Python backend developer, FastAPI, RESTful APIs, Azure, LLM APIs, AI agent frameworks, LangChain, AutoGen, Semantic Kernel, cloud services, vector databases, RAG workflows

job Description : 

Role: Backend Python Developer – AI/ML

Orlando preferable, Las Vegas possible (Onsite)

Required Skills & Experience:

  • 5+ years of experience as a Python backend developer
  • Solid hands-on experience with FastAPI, Flask, or Django REST frameworks
  • Strong knowledge of RESTful APIs, microservices, and asynchronous programming
  • Hands n Experience with Azure 
  • Experience integrating LLM APIs (OpenAI, HuggingFace Inference API, etc.) into real-world applications
  • Experience with AI agent frameworks such as LangChain, AutoGen, or Semantic Kernel
  • Proven ability to deploy containerized apps using Docker, including LLM inference services and agent-based tools
  • Familiarity with vector databases and RAG workflows
  • Exposure to cloud services (AWS, GCP, Azure) and CI/CD pipelines