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Vector Databases Jobs in Bridgewater, MA (NOW HIRING)

Compass

Senior Software Engineer I

Boston, MA · On-site

$149K - $166K/yr

Hands-on GenAI Experience: 1+ years of experience building applications utilizing LLMs, including familiarity with prompt engineering, RAG, vector databases, and popular orchestration frameworks (e.g ...

Mendix

Presales Solutions Architect - Mendix

Boston, MA · On-site

$135K - $270K/yr

Hands-on experience with Generative AI technologies including LLMs, AI agents, RAG architectures, vector databases, and enterprise AI platforms. * Experience integrating AI services with enterprise ...

Pwc

ERP AI Engineer - Manager

Boston, MA · On-site

$99K - $232K/yr

... with vector databases and semantic search architectures - Translating complex business problems into AI solution designs - Contributing to business development and proposal writing - Cloud ...

New Balance

Principal Software Engineer - AI

Boston, MA · On-site

$120K - $150K/yr

Vector Database Proficiency: Experience with specialized vector stores such as Azure AI Search. * Agentic Memory & State: Familiarity with implementing persistent memory and long-term state ...

New Balance

Principal Software Engineer - AI

Boston, MA · On-site

$120K - $150K/yr

Vector Database Proficiency: Experience with specialized vector stores such as Azure AI Search. * Agentic Memory & State: Familiarity with implementing persistent memory and long-term state ...

Humana

Lead Solutions Architect

Boston, MA · On-site

Proven ability to integrate AI systems with event-driven microservices, vector databases/feature stores, and CI/CD pipelines, while leading reference architectures and cross-cloud patterns that scale ...

Humana

Lead Solutions Architect

Boston, MA

Proven ability to integrate AI systems with event-driven microservices, vector databases/feature stores, and CI/CD pipelines, while leading reference architectures and cross-cloud patterns that scale ...

Humana

Lead Solutions Architect

Boston, MA

Proven ability to integrate AI systems with event-driven microservices, vector databases/feature stores, and CI/CD pipelines, while leading reference architectures and cross-cloud patterns that scale ...

Foley

Senior DevOps Engineer

Boston, MA · Remote

$150K - $190K/yr

Experience supporting AI/ML infrastructure, model-serving environments, vector databases, or agent-based platforms. * Familiarity with GitHub Advanced Security, Snyk, Semgrep, OWASP ZAP, or similar ...

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All JobsVector Databases JobsVector Databases Jobs in Bridgewater, MA

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 cities near Bridgewater, MA are hiring for Vector Databases jobs?

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

Vector Databases jobs near you

Senior Software Engineer I

Compass

Boston, MA • On-site

$149K - $166K/yr

Full-time

Re-posted 8 days ago

Compass Real Estate rating

9.3

Company rating: 9.3 out of 10

Based on 7 frontline employees who took The Breakroom Quiz

5th of 204 rated real estate companies

Job description

As a Senior Software Engineer on the Staff AI Enablement Team, you will be a key driver of Compass's enterprise AI strategy as part of a forward-deployed strike force that partners directly with major business units (such as Finance, Legal, HR, and Brokerage Operations, etc.) to produce game-changing, automated workflows.

In this role, you will bridge the gap between complex AI capabilities and real-world business problems. You will design, build, and deploy the direct integrations, custom agents, and automated pipelines that allow our departments to transition from simple chat interfaces to deep, programmatic workflow automations. You will act as a critical technical anchor ensuring code quality, mentoring engineers, enforcing security compliance, and maintaining a high delivery velocity.

Responsibilities:

  • Build Direct Business Integrations: Partner with departmental Subject Matter Experts (SMEs) to translate manual, high-volume operational tasks (e.g., transaction management, legal review, financial reconciliation) into robust, automated AI pipelines.
  • Orchestrate Agentic Workflows: Implement advanced application-level AI patterns, including multi-agent orchestration, RAG pipelines, semantic document routers, and state-machine-driven automation.
  • Utilize and Inform Platform Primitives: Build your integrations on top of our Tier 1 AI Platform APIs, providing continuous feedback loops to the Platform Team to help shape company-wide infrastructure and gateway design.
  • Enforce Rigorous Security Compliance: Ensure all departmental integrations adhere to absolute data security and privacy standards, keeping sensitive company and customer PII strictly protected.
  • Maintain Code Quality & Governance: Review code submissions, establish robust testing frameworks for LLM outputs, and ensure that our rapid execution model does not accumulate technical debt.
  • Unblock and Mentor Engineers: Provide technical guidance to engineers within your dedicated pod, fostering a high-performing and inclusive engineering culture.
  • Focus on Continuous Improvement: Design, monitor, and improve operational metrics and logging for your integrations to quickly troubleshoot, resolve defects, and optimize user experience.

Requirements: 

  • Strong Application & Backend Engineering Background: 6+ years of professional software engineering experience, with a proven track record of designing, building, and operating production-grade web applications, API integrations, or business-critical backend services.
  • Hands-on GenAI Experience: 1+ years of experience building applications utilizing LLMs, including familiarity with prompt engineering, RAG, vector databases, and popular orchestration frameworks (e.g., LangChain, LlamaIndex, LangGraph, AutoGen).
  • High Customer Empathy & Collaboration: Proven experience working closely with cross-functional stakeholders, non-technical partners, or product managers to scope, design, and launch user-centric software solutions.
  • Data Privacy & Security Mindset: A strong understanding of secure coding practices, API authentication (OAuth), and protocols for handling highly sensitive or regulated business data (PII, financial records).
  • Distributed Systems & Integration Expertise: Solid experience with RESTful/GraphQL APIs, message brokers (e.g., Kafka, RabbitMQ), asynchronous job processing, and integrating third-party SaaS platforms.
  • Cloud & DevOps Proficiency: Comfortable deploying and monitoring applications in AWS or similar cloud environments, utilizing Docker, CI/CD pipelines, and modern infrastructure observability tools.
  • Pragmatic Problem Solver: Ability to make smart technical trade-offs, prioritizing immediate business value and operational stability over speculative, over-engineered architectures.

Compensation: The base pay range for this position is $149,580-$166,200; however, base pay offered may vary depending on job-related knowledge, skills, and experience. Bonuses and restricted stock units may be provided as part of the compensation package, in addition to a full range of benefits. Base pay is based on market location. Minimum wage for the position will always be met.

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