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Vector Databases Jobs in Claymont, DE (NOW HIRING)

Accord Technologies Inc.

Senior AI Engineer

Philadelphia, PA · On-site

$123K - $163K/yr

Work with vector databases for embedding and retrieval tasks. * Integrate LLM APIs (e.g., Anthropic Claude, OpenAI GPT, Azure OpenAI) with prompt engineering, guardrails, and citation mechanisms.

SID Global Solutions

AI Lead Developer

Exton, PA · On-site

$57 - $74.50/hr

Develop RAG-based applications leveraging vector databases andenterprise knowledge sources. * Build scalable APIs and AI microservices using Python, FastAPI, andcloud-native architectures. * Deploy ...

SID Global Solutions

AI Lead Engineer

Exton, PA · On-site

$98K - $130K/yr

Develop RAG-based applications leveraging vector databases and enterprise knowledge sources. * Build scalable APIs and AI microservices using Python, FastAPI, and cloud-native architectures. * Deploy ...

SID Global Solutions

AI Lead Developer

Exton, PA · On-site

$57 - $74.50/hr

Develop RAG-based applications leveraging vector databases and enterprise knowledge sources. Build scalable APIs and AI microservices using Python, FastAPI, and cloud-native architectures. Deploy AI ...

State Street Global Advisors

AI Orchestration Engineer

Berwyn, PA

$120K - $202K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Experience with vector databases, semantic search, and enterprise RAG platforms. * Experience implementing MLOps, LLMOps, AI observability, and evaluation frameworks. * Knowledge of Responsible AI ...

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All JobsVector Databases JobsVector Databases Jobs in Claymont, DE

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 Claymont, DE?

For Vector Databases jobs in Claymont, DE, the most frequently searched job titles are:

What job categories do people searching Vector Databases jobs in Claymont, DE look for?

The top searched job categories for Vector Databases jobs in Claymont, DE are:

Vector Databases jobs near you

Software Developer / Engineer - Philadelphia, PA (Locals Only)

Apetan Consulting llc

Philadelphia, PA • On-site

$80 - $150/hr

Contractor

Posted 24 days ago

Job description

Software Developer / Engineer
Location: Philadelphia, PA 
Work Schedule: Hybrid 3 days on site, 2 remote


Position Overview

We are seeking a Software Developer / Engineer to help design and implement an on-premises Large Language Model (LLM) platform with Retrieval-Augmented Generation (RAG) capabilities. This role will focus on deploying open-source AI models, integrating vector databases, and building secure, enterprise-grade AI solutions in a private environment.

This is an excellent opportunity for a developer with hands-on experience in modern AI technologies who enjoys building scalable, high-performance systems.

Responsibilities

  • Deploy and optimize open-source large language models (LLMs) such as Meta Llama 3 and Mistral/Mixtral in on-premises or private environments.
  • Develop Python-based applications for LLM inference, prompt engineering, and model integration.
  • Optimize CPU-based model inference through quantization and performance tuning.
  • Design and implement Retrieval-Augmented Generation (RAG) (RAG) pipelines.
  • Configure and manage open-source vector databases such as Qdrant, Chroma, Milvus, or pgvector.
  • Generate and manage embeddings while implementing metadata filtering strategies.
  • Support enterprise security requirements, including air-gapped deployments, access controls, data privacy, and audit logging.
  • Produce technical documentation, deployment guidance, and knowledge transfer materials for internal teams.
  • Build a working prototype integrating an LLM, vector database, and RAG architecture.

Required Qualifications

  • Professional experience deploying open-source LLMs (e.g., Meta Llama 3, Mistral/Mixtral) in on-premises or private environments.
  • Strong Python development experience.
  • Hands-on experience with LLM inference, prompt engineering, and AI application integration.
  • Experience optimizing CPU-based inference through model quantization and performance tuning.
  • Experience with vector databases such as Qdrant, Chroma, Milvus, or pgvector.
  • Proven experience implementing Retrieval-Augmented Generation (RAG) solutions.
  • Understanding of enterprise security, data privacy, air-gapped environments, access controls, and audit logging.

Preferred Qualifications

  • Experience with LangChain or LlamaIndex.
  • Familiarity with Docker and Kubernetes.
  • Experience with inference frameworks such as vLLM, llama.cpp, or Hugging Face Transformers.
  • Experience with Rust, Go, or C++.
  • Previous experience working in enterprise or regulated environments.

Deliverables

  • Reference architecture and deployment guidance.
  • Working prototype integrating an LLM, vector database, and RAG solution.
  • Technical documentation and knowledge transfer to internal teams.