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Vector Databases Jobs in Lowell, MA (NOW HIRING)

AI Orchestration Engineer

Quincy, MA · On-site

$120K - $202K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Experience with vector databases, semantic search, and enterprise RAG platforms. * Experience implementing MLOps, LLMOps, AI observability, and evaluation frameworks. * Knowledge of Responsible AI ...

AI Orchestration Engineer

Quincy, MA

$120K - $202K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Experience with vector databases, semantic search, and enterprise RAG platforms. * Experience implementing MLOps, LLMOps, AI observability, and evaluation frameworks. * Knowledge of Responsible AI ...

Sr. AI Developer, VP

Burlington, MA · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

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 - AWS Platform

Boston, MA · On-site +1

$130K - $190K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

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

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

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 ...

AI Developer, SA

Burlington, MA · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

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 ...

AI Architect

Newton, MA · Remote

$180K - $220K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Strong experience with RAG architectures, vector databases, and Azure AI services (Azure AI Search, Azure Document Intelligence). * Experience working with evaluation concepts and frameworks such as ...

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

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 ...

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 ...

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 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:

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 AI Solution Architect

Boston Scientific Gruppe

Marlborough, MA • On-site

$106.80 - $202.90/hr

Other

Re-posted yesterday


Job description

Additional Location(s): US-MA-Marlborough; US-MN-Arden Hills

Diversity - Innovation - Caring - Global Collaboration - Winning Spirit- High Performance

About the role:

Boston Scientific is seeking a Senior AI Solution Architect to join our AI Engineering team and lead the design of next-generation AI solutions across the enterprise. In this role, you will operate at the intersection of business strategy and advanced technology - translating complex business challenges into scalable, secure and compliant generative AI and agentic AI architectures. You will define end‑to‑end technical solution architectures for AI‑powered products, including custom generative AI applications, intelligent agents, virtual assistants and reusable AI services. This role requires deep technical expertise, strong architectural judgment and the ability to influence cross‑functional stakeholders across engineering, data, cybersecurity, legal and business teams.

Work model, sponsorship, relocation:

At Boston Scientific, we value collaboration and synergy. This role follows a hybrid work model requiring employees to be in our Minnesota or Massachusetts office at least three days per week. Boston Scientific will not offer sponsorship or take over sponsorship of an employment visa for this position at this time. Relocation assistance is not available for this position at this time.

Your responsibilities will include:
  • Lead the end‑to‑end architecture of enterprise AI solutions, including generative AI applications, large language model‑powered workflows, agentic systems and intelligent automation.
  • Design modular and reusable AI components and services leveraged across multiple platforms and business use cases.
  • Define architectural patterns for agent orchestration, tool integration, memory management, retrieval‑augmented generation and human‑in‑the‑loop workflows.
  • Translate business requirements into scalable, production‑ready AI architectures aligned with enterprise standards.
  • Partner with business stakeholders to understand objectives, constraints and value drivers, ensuring measurable business impact.
  • Collaborate with AI engineers, software engineers, data scientists and data engineers to guide implementation and ensure architectural integrity.
  • Partner with enterprise architecture, cybersecurity, legal, privacy, quality and platform engineering teams to ensure solutions meet regulatory, security and quality expectations.
  • Architect secure and scalable data pipelines in partnership with data engineering teams to support AI and generative AI workloads.
  • Evaluate and integrate technologies across Azure, AWS and Snowflake to deliver cloud‑native, resilient and cost‑effective solutions.
  • Guide platform‑level decisions related to model hosting, vector databases, orchestration frameworks, monitoring and MLOps/LLMOps practices.
  • Ensure solutions are designed for performance, reliability, observability and operational excellence.
  • Embed ethical AI, security‑by‑design, privacy‑by‑design and compliance‑by‑design principles into all solution architectures.
  • Support risk assessments, model reviews and required documentation for enterprise and regulated environments.
Required qualifications:
  • Minimum Bachelor's or Master's degree in computer science, engineering, data science or a related technical field.
  • Minimum of 5 years' experience in solution architecture, software architecture or AI/ML engineering, including recent hands‑on work in generative AI.
  • Proven experience designing and deploying large language model‑based solutions, including retrieval‑augmented generation, prompt engineering and model integration.
  • Previous background in healthcare, life sciences or other highly regulated industries.
  • Strong understanding of cloud‑native architectures in Azure and/or AWS and modern data platforms such as Snowflake.
  • Demonstrated experience working in enterprise‑scale, regulated environments with security, compliance and quality requirements.
  • Demonstrated ability to communicate complex technical concepts clearly to technical and nontechnical audiences.
Preferred qualifications:
  • Proven experience with agentic AI frameworks such as LangGraph, Semantic Kernel, AutoGen, CrewAI or similar technologies.
  • Familiarity with vector databases, embedding strategies and search optimization techniques.
  • Preferred hands‑on experience with MLOps/LLMOps, including model monitoring, evaluation and lifecycle management.
  • Proven experience defining reference architectures, design patterns and reusable AI platforms.

Requisition ID: 631216

Minimum Salary: $106,800

Maximum Salary: $202,900

The anticipated compensation listed above and the value of core and optional employee benefits offered by Boston Scientific (BSC) — see www.bscbenefitsconnect.com — will vary based on actual location of the position and other pertinent factors considered in determining actual compensation for the role. Compensation will be commensurate with demonstrable level of experience and training, pertinent education including licensure and certifications, among other relevant business or organizational needs. At BSC, it is not typical for an individual to be hired near the bottom or top of the anticipated salary range listed above.

Compensation for non‑exempt (hourly), non‑sales roles may also include variable compensation from time to time (e.g., overtime and shift differential) and annual bonus target (subject to plan eligibility and other requirements).

Compensation for exempt, non‑sales roles may also include variable compensation, i.e., annual bonus target and long‑term incentives (subject to plan eligibility and other requirements).

Boston Scientific Corporation has been and will continue to be an equal opportunity employer. To ensure full implementation of its equal employment policy, the Company will continue to take steps to assure that recruitment, hiring, assignment, promotion, compensation and all other personnel decisions are made and administered without regard to race, religion, color, national origin, citizenship, sex, sexual orientation, gender identity, gender expression, veteran status, age, mental or physical disability, genetic information or any other protected class.

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