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Vector Databases Jobs in Bridgewater, MA (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 ...

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 ...

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 ...

SevenAI

Senior AI Engineer

Boston, MA · On-site

$113K - $155K/yr

Tool fluency - comfortable with RAG, vector databases (e.g., Pinecone/Weaviate), workflow frameworks (LangChain, Dust), and related tooling. * Architectural thinker - you can diagram end-to-end ...

The Mutual Group

Lead AI Engineer - AWS Platform

Boston, MA · On-site +1

$130K - $190K/yr

Build RAG pipelines using vector databases and enterprise data sources * Build machine learning models that automate their training, validation, monitoring, and retraining * Develop APIs and services ...

Tata Consultancy Service Limited

AI Architect

Quincy, MA · On-site

$120K - $130K/yr

... vector databases, and orchestration • Proven track record designing secure and compliant systems in regulated environments • Ability to engage CXO stakeholders and influence architecture ...

Tata Consultancy Service Limited

AI Architect

Quincy, MA · On-site

$120K - $130K/yr

... vector databases, and orchestration • Proven track record designing secure and compliant systems in regulated environments • Ability to engage CXO stakeholders and influence architecture ...

FocusKPI Inc.

Junior AI/ML Engineer (Remote)

Boston, MA · Remote

Knowledge of vector databases or embeddings * Familiarity with AWS, GCP, or Azure * Prior internship or project experience building AI/ML applications How to Apply Please submit the following to danz ...

Winaxis

AI/ML Engineer

Boston, MA · On-site

$35 - $45/hr

Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS) * Experience in RAG (Retrieval-Augmented Generation) implementations. * Knowledge of MLOps tools and CI/CD pipelines. * Experience with ...

The Hollister Group

AI Developer

Boston, MA · On-site

$70 - $110/hr

Build retrieval-augmented generation (RAG) systems using Azure AI Search , Amazon Kendra , or vector databases like Pinecone, Weaviate, or FAISS . * Deploy and manage models on Azure Machine Learning ...

Northeastern University

Senior Data Architect

Boston, MA · On-site

$130K - $189K/yr

Knowledge of AI/ML foundational components: vector databases, feature stores, RAG pipelines, metadata management. * Strong understanding of data modeling (conceptual, logical, physical), master data ...

NorthEastern

Senior Data Architect

Boston, MA

$130K - $189K/yr

Knowledge of AI/ML foundational components: vector databases, feature stores, RAG pipelines, metadata management. * Strong understanding of data modeling (conceptual, logical, physical), master data ...

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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 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 job categories do people searching Vector Databases jobs in Bridgewater, MA look for? The top searched job categories for Vector Databases jobs in Bridgewater, MA are:
What cities near Bridgewater, MA are hiring for Vector Databases jobs? Cities near Bridgewater, MA with the most Vector Databases job openings:
Vector Databases jobs near you

GenAI / NLP Developer

Prophecy Technologies

Woonsocket, RI • On-site

Full-time

Posted 15 days ago

Job description

Job Summary
We are seeking an experienced GenAI / NLP Developer with strong Python and deep learning expertise to design, develop, and deploy Large Language Model (LLM)-based solutions. The role focuses on Generative AI, NLP applications, vector databases, and modern AI frameworks, with exposure to cloud platforms and agentic architectures.
Key Responsibilities
  • Develop, fine-tune, and deploy LLM-based applications using Python.
  • Implement Generative AI solutions using prompt engineering techniques and vector databases.
  • Build, train, and optimize NLP models including text classification, sentiment analysis, and summarization.
  • Utilize frameworks such as LangChain or similar tools for LLM orchestration.
  • Support rapid application development using Streamlit, FastAPI, or Flask.
  • Collaborate with cross-functional teams in an Agile development environment.

Required Skills & Experience
  • 6+ years of overall experience in software development, data analytics, or data science.
  • 2+ years of hands-on experience with deep learning, NLP, and GenAI technologies.
  • Strong proficiency in Python programming.
  • Experience with deep learning frameworks such as TensorFlow and PyTorch.
  • Hands-on experience with Hugging Face Transformers.
  • Practical experience with vector databases and prompt engineering.
  • Solid understanding of Large Language Models (LLMs) and real-world AI applications.
  • Experience deploying AI/ML solutions on Azure or GCP (preferred).
  • Knowledge of agentic or multi-agent architectures (e.g., AutoGen) is a plus.
  • Healthcare domain knowledge is a plus.

Competencies
  • Strong problem-solving and analytical skills
  • Ability to work independently and in collaborative Agile teams
  • Excellent communication and documentation skills
  • Innovative mindset with focus on practical AI solutions

Preferred Skills
  • Experience with AI & GenAI solutions for Business Process Services (BPS)
  • Exposure to cloud-native AI deployments
  • Familiarity with rapid prototyping and proof-of-concept development

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