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Vector Databases Jobs in Londonderry, NH (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 ...

Merck Group

Staff Software Engineer

Burlington, MA · On-site

$110K - $165K/yr

Vector Databases like Qdrant Nice to have Skills: * Experience with Kubernetes and container orchestration. * Familiarity with event-driven architectures and messaging platforms such as Kafka.

State Street Global Advisors

AI Developer, AVP

Burlington, MA · On-site

$54.75 - $75.25/hr

Implement RAG pipelines using enterprise data sources and vector databases * Develop and integrate multi-agent systems using MCP servers, APIs, andA2Abased tooling * Embed AI capabilities into core ...

State Street Corporation

AI Developer, AVP

Burlington, MA · On-site

$54.75 - $75.25/hr

Implement RAG pipelines using enterprise data sources and vector databases * Develop and integrate multi-agent systems using MCP servers, APIs, and A2A based tooling * Embed AI capabilities into core ...

NorthEastern

Sr Research Scientist

Burlington, MA · On-site

$107K - $136K/yr

Experience with Retrieval-Augmented Generation (RAG) architectures, vector databases, embedding pipelines, and LLM-integrated systems. * Strong background in network science and graph analytics ...

Northeastern University

Sr Research Scientist

Burlington, MA · On-site

$107K - $136K/yr

Experience with Retrieval-Augmented Generation (RAG) architectures, vector databases, embedding pipelines, and LLM-integrated systems. * Strong background in network science and graph analytics ...

UKG, Inc.

Staff AI FinOps Governance Lead

Lowell, MA · On-site

This role requires someone who understands both cloud economics and the emerging cost drivers of Generative AI, including LLMs, inference services, vector databases, GPUs, and AI agent workloads. Key ...

Optimizely

Forward Deployed Engineer

Nashua, NH · On-site

Familiarity with RAG concepts and experience with building tools/applications for RAG workflows (e.g., interacting with vector databases, embedding services, prompt engineering). Prompting ...

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All JobsVector Databases JobsVector Databases Jobs in Londonderry, NH

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.
Vector Databases jobs near you

Full Stack AI Engineer

BIRDSVUE LLC

Dunstable, MA • Remote

$60 - $70/hr

Full-time

Retirement

Posted 14 days ago

Job description

Benefits:
  • 401(k)
  • Competitive salary

Full Stack AI EngineerIntroduction: We are seeking a highly motivated Full Stack AI Engineer who can design, build, and scale production-grade AI applications from concept to deployment. You will work directly with product leadership and customers to create intelligent systems leveraging LLMs, AI agents, Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), and modern cloud-native architectures.
Responsibilities:
AI & Agentic Systems
  • Design and develop AI-powered applications using OpenAI, Azure OpenAI, Anthropic, Gemini, and open-source models.
  • Build multi-agent and agentic workflows using LangGraph, LangChain, Semantic Kernel, CrewAI, or equivalent frameworks.
  • Develop Retrieval-Augmented Generation (RAG) solutions using embeddings, vector databases, document indexing, and knowledge retrieval systems.
  • Integrate AI models with enterprise systems, APIs, SaaS platforms, and business workflows.
  • Design prompt orchestration, evaluation frameworks, guardrails, observability, and AI governance controls.
Full Stack Development
  • Build scalable frontend applications using React, Next.js, TypeScript, and modern UI frameworks.
  • Develop backend services, APIs, microservices, and event-driven architectures using Python (FastAPI), Node.js, or .NET.
  • Design and optimize SQL and NoSQL databases.
  • Implement authentication, authorization, and secure enterprise-grade integrations.
  • Create reusable APIs and SDKs for AI capabilities across multiple products.
Cloud & Platform Engineering
  • Deploy AI applications on Azure, AWS, or Google Cloud Platform.
  • Build containerized services using Docker and Kubernetes.
  • Implement CI/CD pipelines, infrastructure-as-code, monitoring, and observability.
  • Optimize AI infrastructure for scalability, performance, and cost efficiency.
Product & Innovation
  • Partner with product managers and customers to translate business challenges into AI solutions.
  • Rapidly prototype and productionize AI use cases.
  • Evaluate emerging AI technologies and recommend adoption strategies.
  • Contribute to AI platform roadmap and innovation initiatives.
Requirements:
Required Qualifications:
  • Bachelor''s degree in Computer Science, Engineering, or related field.
  • 5+ years of full-stack software development experience.
  • Strong proficiency in: 
    • Python
    • TypeScript / JavaScript
    • React / Next.js
    • Node.js or FastAPI
  • Experience building REST APIs and microservices.
  • Hands-on experience with: 
    • OpenAI / Azure OpenAI APIs
    • RAG architectures
    • Vector databases (Pinecone, Weaviate, FAISS, Chroma, Azure AI Search)
    • LangChain, LangGraph, Semantic Kernel, or similar frameworks
  • Experience with Docker, Kubernetes, and cloud-native architectures.
  • Strong understanding of software engineering best practices, testing, CI/CD, and Agile development.

This is a remote position.