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Vector Databases Jobs in Lowell, MA (NOW HIRING)

BIRDSVUE LLC

Full Stack AI Engineer

Dunstable, MA · Remote

$60 - $70/hr

Develop Retrieval-Augmented Generation (RAG) solutions using embeddings, vector databases, document indexing, and knowledge retrieval systems. * Integrate AI models with enterprise systems, APIs ...

State Street

Sr. AI Developer, VP

Burlington, MA · On-site

$59.25 - $78.25/hr

... vector databases • Develop and integrate multi-agent systems using MCP servers, APIs, and A2A based tooling • Embed AI capabilities into core CRD and Alpha workflows across front, middle, and ...

S&P Global

Machine Learning Engineer II

Cambridge, MA · On-site

Design and implement end-to-end RAG pipelines that integrate proprietary chunking algorithms, embedding models, vector databases, and data retrieval agents * Build and optimize retrieval systems over ...

Motorola

Senior AI Engineer (US Remote)

Waltham, MA · Remote

$180K - $210K/yr

OpenSearch & Vector Databases * RAG (Retrieval-Augmented Generation) architectures * Agentic frameworks (LangChain, LlamaIndex, or AWS Bedrock Agents) Development Stack: * Python (AI/ML development ...

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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 are popular job titles related to Vector Databases jobs in Lowell, MA?

For Vector Databases jobs in Lowell, MA, the most frequently searched job titles are:

What job categories do people searching Vector Databases jobs in Lowell, MA look for?

The top searched job categories for Vector Databases jobs in Lowell, MA are:

What cities near Lowell, MA are hiring for Vector Databases jobs?

Cities near Lowell, MA with the most Vector Databases job openings:

Vector Databases jobs near you
Infographic showing various Vector Databases job openings in Lowell, MA as of June 2026, with employment types broken down into 64% Full Time, 29% Part Time, 3% Temporary, 3% Contract, and 1% Nights. Highlights an 74% Physical, 3% Hybrid, and 23% Remote job distribution.

Senior Fullstack Engineer, Boston

Cambridge, MA • On-site

Venus Consultancy
See more jobs at Venus Consultancyexternal

Full-time

Re-posted 15 days ago

Job description

About Us

We are a fast-growing GenAI startup building intelligent, production-grade applications powered by large language models and modern cloud infrastructure. Our mission is to transform how businesses interact with data and automation through AI-first products.

Role Overview

We are seeking a talented Full Stack Engineer to help design, build, and scale our GenAI platform. You will work across frontend, backend, and AI-integrated services, collaborating closely with product, ML, and design teams to deliver impactful user experiences.

Responsibilities

  • Design and develop scalable frontend applications using React / Next.js (or similar frameworks)

  • Build backend APIs and services using Python, Node.js, or Go

  • Integrate GenAI/LLM workflows (OpenAI, Anthropic, HuggingFace, etc.) into production systems

  • Develop secure, high-performance REST/GraphQL APIs

  • Work with vector databases and traditional data stores (Postgres, Redis, Pinecone, Weaviate, etc.)

  • Implement authentication, authorization, and observability best practices

  • Deploy and maintain cloud infrastructure on AWS/GCP/Azure

  • Collaborate with ML engineers on model serving and inference pipelines

  • Participate in architecture discussions and contribute to technical direction

  • Own features end-to-end—from concept to production

Required Qualifications

  • Strong experience in Full Stack development (frontend + backend)

  • Proficiency in JavaScript/TypeScript and one backend language (Python preferred)

  • Experience with modern frontend frameworks (React/Next.js/Vue)

  • Hands-on experience building APIs and microservices

  • Familiarity with cloud platforms and CI/CD pipelines

  • Solid understanding of databases and system design

  • Passion for startups, ownership, and rapid iteration

Nice to Have

  • Experience working with LLMs or GenAI frameworks (LangChain, LlamaIndex, etc.)

  • Knowledge of vector databases and RAG pipelines

  • DevOps experience (Docker, Kubernetes, Terraform)

  • Prior startup experience

What We Offer

  • Opportunity to build cutting-edge GenAI products from the ground up

  • High ownership and real impact on product direction

  • Collaborative, fast-paced startup culture

  • Competitive salary + equity

  • Flexible work environment

  • Learning budget and career growth opportunities