ZipRecruiter

Log In

1

Vector Databases Jobs in Peabody, MA (NOW HIRING)

DATAECONOMY

Gen AI Engineer

Boston, MA · On-site

Integrate agents with vector databases, enterprise messaging systems, and internal workflow tools. * Instrument services for observability -- structured logging, tracing, metrics. * Participate in ...

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 ...

S&P Global

Machine Learning Engineer II

Cambridge, MA · On-site

Design and implement end-to-end RAG pipelines that integrate proprietary chunking algorithms, embedding models, vector databases, and data retrieval agents * Build and optimize retrieval systems over ...

Merck KgaA

Senior Data & ML Ops Engineer

Boston, MA · On-site

$137K - $206K/yr

Enable GenAI use cases involving embeddings, vector databases, retrieval-augmented generation, prompt management, and evaluation workflows. * Establish reusable engineering patterns, CI/CD practices ...

Motorola

Senior AI Engineer (US Remote)

Waltham, MA · Remote

$180K - $210K/yr

OpenSearch & Vector Databases * RAG (Retrieval-Augmented Generation) architectures * Agentic frameworks (LangChain, LlamaIndex, or AWS Bedrock Agents) Development Stack: * Python (AI/ML development ...

next page

Showing results 1-20

All JobsVector Databases JobsVector Databases Jobs in Peabody, 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 are popular job titles related to Vector Databases jobs in Peabody, MA?

For Vector Databases jobs in Peabody, MA, the most frequently searched job titles are:

What job categories do people searching Vector Databases jobs in Peabody, MA look for?

The top searched job categories for Vector Databases jobs in Peabody, MA are:

What cities near Peabody, MA are hiring for Vector Databases jobs?

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

Vector Databases jobs near you
Infographic showing various Vector Databases job openings in Peabody, MA as of August 2026, with employment types broken down into 82% Full Time, 11% Part Time, 1% Temporary, and 6% Contract. Highlights an 83% Physical, 6% Hybrid, and 11% Remote job distribution.

Gen AI Engineer

DATAECONOMY

Boston, MA • On-site

Other

Posted 5 days ago

Job description

GenAI Platform Engineer

Boston, MA

Full-time role

Experience: 10+yrs

 

About the Role

We''''re looking for a Senior GenAI Platform Engineer to build and operate our enterprise AI agent platform — hands-on, in the code, every day. This is an individual contributor role with no direct reports. You''''ll design and build durable asynchronous orchestration, RAG pipelines, and secure multi-agent workflows on AWS, working alongside a small team of peer engineers. You''''ll participate in architecture discussions and code reviews as a strong technical contributor, not as a manager.

Education & Experience

  • Bachelor’s degree in computer science, Information Systems, Engineering, or equivalent practical experience.
  • 5–8 years of professional software engineering experience.
  • 2+ years of hands-on experience building GenAI/Agentic AI applications in production.

What You''''ll Do

  • Build AI agents and multi-agent workflows using Amazon Bedrock, Bedrock Agents, AgentCore, LangChain, and LangGraph.
  • Design and implement RAG pipelines, prompt engineering strategies, and AI guardrails.
  • Build backend services in FastAPI for agent submission, execution, lifecycle state machines, and status tracking.
  • Implement queue-based dispatch and durable state stores, ensuring idempotency, retries, and failure recovery.
  • Write Infrastructure as Code (Terraform) to provision and manage agent infrastructure on AWS.
  • Integrate agents with vector databases, enterprise messaging systems, and internal workflow tools.
  • Instrument services for observability — structured logging, tracing, metrics.
  • Participate in code reviews and architecture discussions as a peer contributor.
  • Write clear technical documentation (design docs, runbooks) for systems you build.

Required Qualifications

  • Expert-level Python, with strong experience building REST APIs (FastAPI preferred).
  • Hands-on experience with LLMs, Amazon Bedrock (Agents & AgentCore), LangChain, LangGraph, RAG, and vector databases.
  • Strong experience with core AWS services: Lambda, ECS/EKS, API Gateway, S3, RDS/PostgreSQL.
  • Working proficiency in Terraform and Docker.
  • Experience with CI/CD pipelines (GitHub Actions, GitLab CI, or Jenkins).
  • Solid understanding of asynchronous programming and event-driven architectures (Kafka or SQS).
  • Experience with modern authentication patterns (OAuth2, OIDC, JWT) and AWS IAM.

Technical skills

  • Programming: Python , FastAPI
  • Generative AI: Amazon Bedrock, Bedrock Agents, AgentCore, LangChain, LangGraph, Prompt Engineering, Retrieval-Augmented Generation (RAG), AI Guardrails, Multi-Agent Systems
  • Vector Databases: Pinecone, Amazon OpenSearch Vector Engine, FAISS, ChromaDB, pgvector
  • Infrastructure as Code: Terraform, AWS CloudFormation (preferred exposure)
  • Containers & Orchestration: Docker
  • Databases: PostgreSQL, DynamoDB
  • Event-Driven Architecture: Amazon SQS, Apache Kafka
  • Authentication & Security: OAuth 2.0, OpenID Connect (OIDC), JWT, AWS IAM, Enterprise Identity Integration (Microsoft Entra ID)
  • CI/CD & DevOps:  Git, Automated Deployment Pipelines
  • Observability: AWS Observability
  • Software Engineering: System Design, Microservices Architecture, Backend Development, API Design, Code Reviews, Technical Documentation, Design Documents, Runbooks
  • Development Practices: Unit Testing, Integration Testing, Code Quality, Secure Coding Practices, Agile/Scrum

 

Preferred Qualifications

  • AWS Associate or Professional certification.

Apply