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Vector Databases Jobs in Bridgewater, MA (NOW HIRING)

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

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 ...

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 ...

InterSystems

AI Application Engineer

Boston, MA

Experience building AI/ML solutions-such as agentic applications, LLM inference, similarity search, vector databases, guardrails, or memory systems. * Strong coding skills in Python or other ...

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All JobsVector Databases JobsVector Databases Jobs in Bridgewater, MA

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 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

Generative AI Engineer

Prophecy Technologies

Woonsocket, RI • On-site

Full-time

Posted 19 days ago

Job description

Role Overview:
This role focuses on the design, development, fine-tuning, and deployment of LLM-based applications using Python. The successful candidate will be instrumental in implementing and optimizing deep learning models for Natural Language Processing (NLP) use cases and deploying AI solutions on cloud platforms like Azure and Google Cloud Platform (GCP).
Key Responsibilities:
  • Design, build, fine-tune, and deploy LLM-based applications using Python.
  • Implement and optimize deep learning models for NLP use cases, including text classification, sentiment analysis, and text summarization.
  • Develop solutions using prompt engineering techniques.
  • Utilize vector databases to store and retrieve model embeddings and AI-generated data.
  • Work with deep learning frameworks and libraries such as TensorFlow, PyTorch, and Hugging Face Transformers.
  • Apply knowledge of deep learning architectures and techniques.
  • Use orchestration frameworks such as LangChain or similar tools.
  • Build AI-powered applications using Streamlit, FastAPI, and Flask.
  • Deploy and scale machine learning models on Azure and Google Cloud Platform (GCP).
  • Write efficient, well-documented, and maintainable Python code.
  • Support CI/CD pipelines for ML and GenAI deployments.
  • Apply knowledge of agentic architectures and multi-agent patterns such as AutoGen or similar frameworks.
  • Collaborate with cross-functional teams to design scalable AI systems.

Required Skills:
  • Strong proficiency in Python.
  • Experience in deep learning, Natural Language Processing (NLP), and Generative AI.
  • Understanding of large language models and their real-world applications.
  • Experience managing AI model lifecycle from development to production.
  • Experience deploying AI solutions on Azure or GCP.
  • Knowledge of healthcare domain workflows and data.
  • Familiarity with agentic and multi-agent AI design patterns.
  • GCP.
  • CI/CD.

Qualifications:
  • 6-8 years of overall experience in software development, data analytics, or data science.
  • 2+ years of hands-on experience with deep learning, NLP, and Generative AI.

Apply