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Vector Databases Jobs in Boston, 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 ...

Blitzy

Principal Software Engineer

Cambridge, MA · On-site

$148K - $199K/yr

MongoDB, Cassandra, DynamoDB, or similar. • Experience with graph databases (Neo4j) for modeling and searching complex relationships. • Experience with vector databases / embeddings ...

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 ...

CVS Health

Principal Software Engineer

Wellesley, MA · On-site

$148K - $198K/yr

Familiarity with vector databases (e.g., Vertex Vector Search, Pinecone, Weaviate, pgvector) and advanced retrieval techniques * Experience designing evaluation frameworks for LLM systems (gold ...

NorthEastern

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 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 ...

Third Way Health

AI / ML Engineer

Cambridge, MA · On-site

Experience designing ML-centric data architectures, including feature stores, vector databases, and time-series systems for monitoring and analytics. * Hands-on experience with cloud-native inference ...

Socotec

Software Engineer

Charlestown, MA

Familiarity with one or more of: agent orchestration frameworks (LangGraph, LangChain), vector databases (Pinecone, Weaviate), or RAG architectures * Experience with modern data infrastructure ...

SOCOTEC

Software Engineer

Boston, MA · On-site

$100K - $130K/yr

Familiarity with one or more of: agent orchestration frameworks (LangGraph, LangChain), vector databases (Pinecone, Weaviate), or RAG architectures * Experience with modern data infrastructure ...

Deloitte

Manager - Data Science / Data Lake

Boston, MA · On-site

... as vector databases, LangChain, or CrewAI • 10+ years of experience using Python, Structured Query Language (SQL), R, or SAS to prepare data for analysis, engineer features, visualize data, or ...

InterSystems

AI Application Engineer

Boston, MA · On-site

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 ...

Granite Telecommunications

Lead AI Engineer

Quincy, MA · On-site

$180K - $280K/yr

Design, develop, and optimize Retrieval-Augmented Generation (RAG) pipelines using advanced vector databases (e.g., FAISS, Pinecone, Milvus). * Build and enhance agentic AI systems utilizing ...

Manulife

Senior AI Engineer

Boston, MA · On-site

$113K - $155K/yr

Experience with vector databases or search technologies such as Azure AI Search, Elastic, Pinecone, FAISS, or similar tools. * Experience building production-grade GenAI applications using ...

Career Site

Lead AI Engineer

Quincy, MA · On-site +1

$180K - $280K/yr

Design, develop, and optimize Retrieval-Augmented Generation (RAG) pipelines using advanced vector databases (e.g., FAISS, Pinecone, Milvus). * Build and enhance agentic AI systems utilizing ...

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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 are popular job titles related to Vector Databases jobs in Boston, MA? For Vector Databases jobs in Boston, MA, the most frequently searched job titles are:
What job categories do people searching Vector Databases jobs in Boston, MA look for? The top searched job categories for Vector Databases jobs in Boston, MA are:
What cities near Boston, MA are hiring for Vector Databases jobs? Cities near Boston, MA with the most Vector Databases job openings:
Vector Databases jobs near you
Junior AI/ML Engineer (Remote)

Junior AI/ML Engineer (Remote)

FocusKPI Inc.

Boston, MA • Remote

Other

Posted 18 days ago

Job description

Location: Remote (EST preferred)
Type: Paid Internship or Contract (Full-time)
Start Date: Immediate

Role Overview

We are seeking a Junior AI/ML Engineer to support the development of an enterprise AI chatbot and data intelligence system. This role will focus on building interactive AI agents that connect to enterprise databases, generate insights, and support decision-making.

The position includes hands-on work in both LLM-based chatbot/agent development and machine learning, including building prediction models and integrating them into production workflows. This is a client-facing technical role requiring strong coding ability, analytical thinking, and the ability to work in a fast-paced environment.

Key Responsibilities
  • Develop and deploy AI chatbot/agent solutions for enterprise use cases
  • Integrate LLM-based agents with structured and unstructured enterprise data
  • Connect chat interfaces to SQL/enterprise databases and internal APIs
  • Build and integrate machine learning models (prediction, classification, clustering, etc.)
  • Perform data preprocessing, feature engineering, and model evaluation
  • Integrate ML outputs into chatbot or workflow interfaces
  • Support data pipeline development and automation
  • Collaborate with internal team and occasionally clients to understand requirements
  • Document model logic, workflows, and implementation details
Required Qualifications
  • Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Engineering, or related quantitative field
  • Strong Python programming skills
  • Hands-on experience with machine learning (regression, classification, clustering, or similar)
  • Familiarity with ML libraries such as scikit-learn, pandas, numpy, or similar
  • Experience working with databases (SQL, PostgreSQL, or similar)
  • Familiarity with LLM APIs (OpenAI, Claude, or open-source models)
  • Experience building APIs or backend services (FastAPI, Flask, or similar)
  • Strong analytical and problem-solving skills
  • Ability to work independently and communicate clearly
Preferred Qualifications
  • Graduate degree or currently pursuing MS in AI, Data Science, or related field
  • Experience with agent frameworks (LangChain, LlamaIndex, LangGraph, CrewAI, etc.)
  • Experience deploying or integrating ML models into applications
  • 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@focuskpi.com:

  1. Resume
  2. GitHub or portfolio that includes at least one agentic or AI chatbot project
  3. A short written explanation (3–6 paragraphs) of one project, including:
    • What problem the agent/chatbot solves
    • Architecture and tools used (LLM, framework, database, etc.)
    • Your specific contribution
    • Challenges encountered and how you solved them

Candidates without project examples or explanation will not be considered.

Immediate start preferred.

NOTICE: Please be aware of fraudulent emails regarding job postings, job offers and fake checks. FocusKPI's recruiting team will strictly reach out via @focuskpi.com email domain. If you have received fraudulent emails now or in the past, please report it to https://reportfraud.ftc.gov/ .
The domain @focuskpijobs.com is fraudulent and not related to FocusKPI. Please do not not reply or communicate to anyone with @focuskpijobs.com.

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About FocusKPI

Sourced by ZipRecruiter

Industry

Computing infrastructure providers, data processing, web hosting

Company size

51 - 200 Employees

Headquarters location

Santa Clara, CA, US

Year founded

2010

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