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Vector Databases Jobs in New York (NOW HIRING)

KPF

Junior Data & Systems Developer

New York, NY · On-site

$73K - $95K/yr

No prior AI experience is required, you will gain hands-on exposure to AI systems, including large language models, retrieval-augmented generation, and vector databases, through direct collaboration ...

Purple Drive Technologies

Sr. AI/ML Engineer

Jersey City, NJ · On-site

$109K - $149K/yr

Fine-tune and deploy LLMs integrated with vector databases (FAISS, Pinecone, ChromaDB). Detailed Description: • Experienced AI/ML Engineer with expertise in Machine Learning, Deep Learning, NLP ...

Schonfeld

AI Data Engineer

Manhattan, NY · On-site

$126K - $151K/yr

... vector databases and semantic search systems. • Design data schemas and storage solutions that support efficient retrieval and processing for LLM applications. • Implement data versioning ...

10a Labs

MLOps / Infrastructure Engineer

New York, NY · Remote

$130K - $230K/yr

Help manage vector databases and semantic search infrastructure (e.g., Pinecone, FAISS, Vertex Matching Engine). * Ensure security, compliance, and uptime of infrastructure supporting safety-critical ...

Purple Drive Technologies

GEN AI Architect

New York, NY · On-site

PyTorch, TensorFlow, Keras • NLP and vector databases • Cloud-native AI (AWS / Azure / GCP) • Docker, Kubernetes • Presales and client-facing solutioning Roles & Responsibilities • Define ...

Zilliz

Enterprise Account Executive - New York

New York, NY · On-site +1

$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 ...

Zilliz

Enterprise Account Executive - New York

New York, NY · 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 ...

Saransh Inc

Sr. AI/ML Engineer - Onsite

Jersey City, NJ · On-site

$109K - $149K/yr

Fine-tune and deploy LLMs integrated with vector databases (FAISS, Pinecone, ChromaDB). Detailed Description: * Experienced AI/ML Engineer with expertise in Machine Learning, Deep Learning, NLP ...

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All JobsVector Databases JobsVector Databases Jobs in New York

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
Infographic showing various Vector Databases job openings in New York as of June 2026, with employment types broken down into 86% Full Time, 8% Part Time, 3% Temporary, and 3% Contract. Highlights an 66% Physical, 4% Hybrid, and 30% Remote job distribution.

Enterprise Architect - AI Project Implementation

Purple Drive Technologies

New York, NY • On-site

$90K - $121K/yr

Full-time

Posted 6 days ago

Job description

Overview:
Role: Enterprise Architect - AI Project Implementation
Location: Hybrid - New York, NY / New Jersey, NJ
Employment Type: Contract
Job Description:
We are seeking a highly experienced Enterprise Architect with a strong background in AI-driven solution design and implementation. The ideal candidate will possess hands-on expertise in data analytics, GenAI orchestration, LLM frameworks, and cloud-based architecture. This role involves working closely with AI leads, data engineers, and product teams to conceptualize, design, and deliver scalable AI systems aligned with enterprise goals.
Key Responsibilities:
  • Lead the architecture, design, and implementation of AI and data-driven solutions across enterprise systems.
  • Translate business requirements into scalable, API-based architectures leveraging modern AI and data technologies.
  • Collaborate with GenAI leads to deliver end-to-end AI solutions, from data ingestion to model integration.
  • Conduct experiments and evaluations on emerging LLMs and AI tools to identify potential enhancements.
  • Provide hands-on guidance in implementing and orchestrating GenAI components using Python, PySpark, or Java.
  • Integrate with Gemini Pro 1.x and similar LLMs via API endpoints for AI feature delivery.
  • Apply prompt engineering techniques and work with LangChain or similar LLM agents.
  • Leverage vector databases such as Pinecone, Chroma, or FAISS for semantic search and contextual retrieval.
  • Design and maintain data pipelines and infrastructure to support real-time AI workloads.
  • Utilize Google Cloud Platform (GCP) services for storage, serverless execution, search, transcription, and conversational AI.
  • Ensure best practices in data security, governance, and performance optimization across systems.
Required Skills & Qualifications:
  • Bachelor's or Master's degree in Computer Science, Data Engineering, or related technical field.
  • 8+ years of experience in software development, data analytics, or enterprise architecture.
  • Proficiency in Python, PySpark, or Java for AI orchestration and API-based development.
  • Proven experience in AI/ML architecture design, particularly GenAI solutions.
  • Hands-on experience with LangChain, LLMs, vector databases (Pinecone/Chroma/FAISS), and prompt engineering.
  • Experience integrating and working with Gemini Pro 1.x or similar LLM frameworks.
  • Solid understanding of data engineering workflows and ETL processes for structured and unstructured data.
  • Experience in GCP cloud services, including BigQuery, Cloud Functions, Vertex AI, and Firestore.
  • Strong debugging, troubleshooting, and optimization skills.
Preferred Skills:
  • Familiarity with AI model lifecycle management, MLOps, and data governance frameworks.
  • Experience in ETL tools and data pipeline automation.
  • Knowledge of multi-cloud architecture (AWS, Azure) is a plus.
  • Prior experience leading AI transformation initiatives or enterprise data modernization projects.

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