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Vector Databases Jobs in Massachusetts (NOW HIRING)

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 ...

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 ...

Lead AI Engineer

Boston, MA · On-site

$111K - $146K/yr

LangGraph, LangChain, LlamaIndex, MCP and Vector Databases • Infrastructure: AWS or GCP, Docker and Kubernetes Company : Mirakl is a SaaS solution that helps enterprises manage their marketplace ...

Preferred : • Geospatial data experience (H3, PostGIS, GeoPandas) • Mobility or location data experience • Embedding-based retrieval (pgvector, FAISS, vector databases) • Bandits, contextual ...

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 ...

AI/ML Engineer

Boston, MA · On-site

$124K - $149K/yr

LangChain LlamaIndex Hugging Face OpenAI APIs Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS) Experience in RAG (Retrieval-Augmented Generation) implementations. Knowledge of MLOps tools and ...

AI/ML Engineer

Boston, MA · On-site

$32 - $35/hr

LangChain LlamaIndex Hugging Face OpenAI APIs Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS) Experience in RAG (Retrieval-Augmented Generation) implementations. Knowledge of MLOps tools and ...

AI/ML Engineer

Boston, MA · On-site

$30 - $35/hr

LangChain LlamaIndex Hugging Face OpenAI APIs Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS) Experience in RAG (Retrieval-Augmented Generation) implementations. Knowledge of MLOps tools and ...

AI/ML Engineer

Boston, MA · On-site

$124K - $149K/yr

LangChain LlamaIndex Hugging Face OpenAI APIs Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS) Experience in RAG (Retrieval-Augmented Generation) implementations. Knowledge of MLOps tools and ...

... vector databases and semantic search technologies · Knowledge of legal, regulatory, or document-intensive data environments · Experience with model evaluation, monitoring, and optimization · ...

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 ...

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 ...

Senior ML Engineer

Boston, MA · On-site

$113K - $155K/yr

... vector databases and semantic search technologies Responsible AI and governance experience Proven ability to influence cross-functional technical direction Experience mentoring senior engineers ...

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

Showing results 41-60

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 Massachusetts look for? The top searched job categories for Vector Databases jobs in Massachusetts are:
What cities in Massachusetts are hiring for Vector Databases jobs? Cities in Massachusetts with the most Vector Databases job openings:
Infographic showing various Vector Databases job openings in Massachusetts as of August 2026, with employment types broken down into 87% Full Time, 5% Part Time, 1% Temporary, and 7% Contract. Highlights an 84% Physical, 5% Hybrid, and 11% Remote job distribution.

Lead AI Engineer - AWS Platform

The Mutual Group

Boston, MA • On-site, Remote

$130K - $190K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 20 days ago


Job description

Department:

Information Technology

Job Description:

We are modernizing our data and analytics ecosystem by embedding AI and Generative AI across core insurance platforms (Policy, Claims, Billing, and Enterprise systems).

We are hiring a Lead AI Engineer to build and scale production-grade AI solutions on AWS. This role is hands-on and focused on delivering real systems, while helping shape the foundation of our emerging AI platform.

This is not a pure research or modeling role. It is an engineering role focused on building, deploying, and operating AI systems in a regulated enterprise environment.

Work Arrangement:

  • Employees who live within 30 miles of the TMG home office are expected to follow a hybrid or in-office schedule. The initial training period may require additional inoffice days.

Accountabilities:

Build AI Systems (Core Responsibility)

  • Design and implement end-to-end AI/ML solutions including LLM-based applications

  • 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 to operationalize AI capabilities across the organization

Develop Data + AI Pipelines

  • Build ingestion for multimodal content and transformation pipelines for structured and unstructured data

  • Integrate AI workflows with enterprise systems (policy, claims, billing, etc.)

  • Ensure data quality, traceability, reliability, and governance in all AI pipelines

Operationalize Models (MLOps)

  • Implement CI/CD for AI/ML workflows

  • Deploy, monitor, and maintain models in production

  • Manage model versioning, performance monitoring, and retraining processes

Build on AWS

  • Develop solutions using: Amazon SageMaker, AWS Lambda, S3, Glue, EKS, and related services

  • Contribute to evolving use of AWS Bedrock

Apply Responsible AI Practices

  • Implement guardrails for LLM-based systems (grounding, validation, safety)

  • Ensure secure handling of sensitive data (PII, financial, etc.)

  • Build systems aligned with enterprise governance and compliance standards

Lead by Doing

  • Provide technical guidance and mentorship to engineers

  • Contribute to engineering standards and reusable patterns

  • Partner with architects and business teams to deliver high-impact use cases

Qualifications:

Required

  • 10+ years in software, data engineering, 5 years AI/ML engineering

  • Hands-on experience building production AI/ML systems

  • Experience with RAG pipelines, LLMs, or NLP-based systems

  • Experience with AWS Bedrock or similar GenAI platforms

  • Experience with data pipelines and distributed systems

  • Experience deploying and operating systems in AWS

  • Working knowledge of MLOps practices (CI/CD, monitoring, versioning)

Preferred

  • Experience with vector databases (Pinecone, Weaviate, etc.)

  • Experience in regulated industries (insurance, finance, healthcare)

  • Exposure to microservices and containerized environments (Docker, Kubernetes)

Pay Range:

Anticipated Hiring Range:

  • $130,000 - $190,000 annual base salary depending on experience, qualifications, and geographic location

Benefits:

We are proud to offer our full-time regular employees a robust benefits suite that includes:

  • Competitive base salary plus incentive plans for eligible team members

  • 401(K) retirement plan that includes a company match of up to 6% of your eligible salary

  • Free basic life and AD&D, long-term disability and short-term disability insurance

  • Medical, dental and vision plans to meet your unique healthcare needs

  • Wellness incentives

  • Generous time off program that includes personal, holiday and volunteer paid time off

  • Flexible work schedules and hybrid/remote options for eligible positions

  • Educational assistance

Equal Opportunity Employer

The Mutual Groupis an Equal Opportunity Employer. It is our policy to recruit, hire, train and promote individuals in all job classifications without regard to race, color, religion, sex, national origin, age, veteran status, disability, sexual orientation, gender identity or any other characteristic protected by law.

  • Know Your Rights: Workplace Discrimination is Illegal

  • Your Rights Under USERRA

Applicants requiring a reasonable accommodation due to a disability at any stage of the employment application process should contactTalent@themutualgroup.com.

Employment Verification

The Mutual Group participates in theE-Verifyprogram and will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S. You are protected fromemployment discriminationbased on your citizenship status and national origin.

E-Verify Program Overview

E-Verify Participation Poster

All offers of employment are contingent upon the successful completion of a background check.

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