ZipRecruiter

Log In

1

Vector Databases Jobs in Massachusetts (NOW HIRING)

Globalchannelmanagement

Remote AI Architect

Boston, MA · Remote

$90 - $92/hr

Experience with MLOps/LLMOps ecosystems, including tools such as MLflow, Kubernetes, LangChain, vector databases, and feature stores. * Strong hands on experience with ML frameworks, LLM platforms ...

QTech US Inc

Gen AI Engineer

Boston, MA · On-site

Work with embeddings and vector databases for semantic search and AI retrieval. Deploy and manage AI applications on Google Cloud Platform, AWS, or Azure (Google Cloud Platform preferred)

New

JLL

Lead AI Engineer

Boston, MA

$111K - $146K/yr

Experience working with vector databases, knowledge graphs, and RAG pipeline development * Advising on best practices for AI agent development and enterprise AI integration processes * Experience in ...

Pfizer

Full Stack AI Solutions Engineer

Cambridge, MA · On-site

Integration of semantic search technologies, vector databases and embedding models to enable intelligent information retrieval * Contribution to the development of novel semantic frameworks and ...

JLL

Senior Software Engineer, AI Platforms

Boston, MA

$133K - $175K/yr

Experience with vector databases, information retrieval systems, and optimizing search performance (highly preferred). * Familiarity with containerization (Docker, Kubernetes) and infrastructure-as ...

next page

Showing results 1-20

All JobsVector Databases JobsVector Databases Jobs in Massachusetts

Vector Databases information

What is the salary of a vector database developer?

The salary of a vector database developer typically ranges from $80,000 to $150,000 annually, depending on experience, location, and company size. Skilled developers with expertise in machine learning, data structures, and database management may earn higher salaries, especially in tech hubs or with advanced certifications.

Are vector databases the future?

Vector database jobs involve managing and optimizing databases designed for high-dimensional vector data, which are essential for AI and machine learning applications. As AI continues to grow, demand for professionals skilled in vector database technologies and related tools like embedding models is expected to increase, making this a promising field for future job opportunities.

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 can you do with a vector database?

A vector database is used in roles involving data management and machine learning to store, search, and retrieve high-dimensional vector representations of data such as images, text, or audio. It enables efficient similarity searches, supporting applications like recommendation systems, natural language processing, and computer vision. Working with a vector database often requires knowledge of data structures, indexing techniques, and programming skills in languages like Python or C++.

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 the top 5 vector databases?

Top vector databases used in data management and AI applications include Pinecone, Weaviate, FAISS, Milvus, and Annoy. These databases are optimized for storing and searching high-dimensional vector data, often requiring skills in machine learning and database management. They are widely adopted for tasks like similarity search and recommendation systems.
What are popular job titles related to Vector Databases jobs in Massachusetts? For Vector Databases jobs in Massachusetts, the most frequently searched job titles are:
What job categories do people searching Vector Databases jobs in Massachusetts look for? The top searched job categories for Vector Databases jobs in Massachusetts are:
What cities in Massachusetts are hiring for Vector Databases jobs? Cities in Massachusetts with the most Vector Databases job openings:
Vector Databases jobs near you
Infographic showing various Vector Databases job openings in Massachusetts as of July 2026, with employment types broken down into 65% Full Time, and 35% Contract. Highlights an 92% In-person, and 8% Remote job distribution.

Remote AI Architect

Globalchannelmanagement

Boston, MA • Remote

$90 - $92/hr

Full-time

Re-posted 11 days ago

Job description

Remote AI Architect needs 10+ years' experience enterprise-wide AI programs or platform buildouts.

AI Architect requires:

  • Strong understanding of data governance, privacy, security, and model risk management.
  • Prior experience with large-scale transformation programs.
  • equired Qualifications
  • Bachelor's degree in Computer Science, Engineering, or a related technical field.
  • 5+ years of experience in application development, engineering, or solution delivery roles.
  • 1+ years of hands-on experience in AI/ML engineering, data science, or AI solution architecture.
  • Strong hands-on experience with machine learning frameworks and LLM platforms (e.g., OpenAI, Azure AI Foundry, Copilot Studio/Agent Builder, or comparable generative AI ecosystems).
  • Deep expertise in cloud platforms, particularly Microsoft Azure, and modern architectural patterns (microservices, event-driven architectures, API-first design).
  • Proficiency in one or more of the following: Python, Azure Machine Learning, or related AI/ML tooling.
  • Experience with MLOps/LLMOps ecosystems, including tools such as MLflow, Kubernetes, LangChain, vector databases, and feature stores.
  • Strong hands on experience with ML frameworks, LLM platforms - OpenAI, MSFT/Azure Cloud foundry, Copilot Studio Agent builder, low code/no code platforms, and generative AI tools.
  • Background in RAG systems, model fine tuning, embeddings, vector storage, and retrieval optimization.

AI Architect duties:

Provide architectural oversight across AI/ML projects to ensure consistency, performance, and maintainability.

Evaluate and select AI technologies, frameworks, cloud services, vector databases, LLM orchestration frameworks, and tooling.

Support development teams on model selection, training pipelines, prompt engineering, fine tuning, RAG (Retrieval-Augmented Generation), and evaluation methodologies.

Mentor engineers, analysts, and product teams on AI best practices.