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Vector Databases Jobs in Seaford, NY (NOW HIRING)

Photon

GenAI Developer / Python

Manhattan, NY · On-site

$55.50 - $76.25/hr

PostgreSQL, Vector Databases, and Advanced Retrieval strategies. ML/DL: PyTorch, TensorFlow, and Model Fine-tuning. Deployment: Docker, Production API management, and LLM monitoring. Tools: Prompt ...

Citigroup, Inc.

Lead Generative AI Developer

New York, NY · On-site

$176K - $265K/yr

Design and optimize data pipelines feeding AI systems, working with vector databases (e.g., Pinecone, Weaviate, pgvector) and enterprise data platforms. * Technical Leadership: Mentor junior ...

2T Consulting

Python Agentic AI Engineer

Brooklyn, NY · On-site

Develop RAG solutions using embeddings and vector databases. * Build and integrate REST APIs, microservices, and event-driven architectures. * Develop scalable backend applications using Python ...

New

2T Consulting

Python Agentic AI Engineer

New York, NY · On-site

Develop RAG solutions using embeddings and vector databases. * Build and integrate REST APIs, microservices, and event-driven architectures. * Develop scalable backend applications using Python ...

New

2T Consulting

Python Agentic AI Engineer

Elmhurst, NY · On-site

Develop RAG solutions using embeddings and vector databases. * Build and integrate REST APIs, microservices, and event-driven architectures. * Develop scalable backend applications using Python ...

New

Virtusa Corporation

Vectorization Expert

New York, NY · On-site

Manage and optimize vector databases (Pinecone, Weaviate, Milvus, pgvector), including building efficient indexing strategies (HNSW, DiskANN). Develop advanced chunking and parsing techniques to ...

2T Consulting

Python Agentic AI Engineer

Flushing, NY · On-site

Develop RAG solutions using embeddings and vector databases. * Build and integrate REST APIs, microservices, and event-driven architectures. * Develop scalable backend applications using Python ...

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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 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 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 cities near Seaford, NY are hiring for Vector Databases jobs?

Cities near Seaford, NY with the most Vector Databases job openings:

Vector Databases jobs near you

Senior Software Developer_2/Generative AI Developer

search-tactics

New York, NY • On-site

$40 - $45/hr

Contractor

Re-posted 18 days ago

Job description

  • Design and develop web applications, APIs, microservices using C#, .NET Core, REST, and modern UI frameworks. 

  • Fine-tuning and deploying Large Language Models (LLM) such as Claude, ChatGPT, or similar models using Amazon Web Services (AWS) Bedrock and Python frameworks 

  • Optimizing, designing and/or deploying Retrieval Augmented Generation (RAG) vector databases. 

  • Optimize database performance, manage stored procedures, and ensure data quality across Oracle and cloud data platforms. 

  • Deploy and monitor applications and models using CI/CD pipelines, Docker, Kubernetes, GitHub Actions, Azure DevOps, Jenkins, etc. and MLOps/LLMOps practices. 

  • Ensure platform security, compliance, logging, observability, and performance tuning across environments. 

  • Participate in requirements review, sprint planning, code reviews, release management, and documentation. 

  • Support production releases, perform troubleshooting, and deliver timely resolution to technical issues. 

Required Skills 

  • Minimum 10 years of experience in Programming: .NET Core, C#, Python 

  • Minimum 5 years of experience in AI/ML/NLP: LangChain, LangGraph, LlamaIndex, NLP models, RAG vector databases, model deployment 

  • Minimum 7 Years of experience with Database: Oracle SQL/PL-SQL, ER design, query optimization. 

  • Minimum 7 Years of experience in Microservices & Web: REST APIs, MVC, Web API, JSON, OAuth, SSO, microservices architecture. 

  • Minimum 7 Years of experience in Cloud & DevOps: Azure, AWS or GCP, Docker, CI/CD, API gateways, including AI-specific services like AWS Bedrock and SageMaker AI 

  • Minimum 10 years of experience with strong debugging, performance tuning & security practices. 

  • Minimum 10 Years of experience with demonstrated experience delivering mid-to-large scale enterprise applications and integrations.