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Vector Databases Jobs in Sterling, MA (NOW HIRING)

Blue Prism Group

Senior Software Engineer

Waltham, MA

$132K - $174K/yr

Work with vector databases, embeddings, semantic search, and AI-driven APIs to build intelligent workflows and enhance product capabilities. * Support the development of AI-assisted features such as ...

SS&C Technologies

Senior Software Engineer

Waltham, MA · On-site

$132K - $174K/yr

Work with vector databases, embeddings, semantic search, and AI-driven APIs to build intelligent workflows and enhance product capabilities. * Support the development of AI-assisted features such as ...

SS&C Technologies

Senior Software Engineer

Waltham, MA

$132K - $174K/yr

Work with vector databases, embeddings, semantic search, and AI-driven APIs to build intelligent workflows and enhance product capabilities. * Support the development of AI-assisted features such as ...

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 ...

SS&C Technologies

Senior ML Engineer - Agentic AI

Waltham, MA · On-site

$112K - $154K/yr

Integrate agents with external services - REST APIs, vector stores, graph databases, and internal SDKs - through well-defined MCP interfaces. CI/CD Pipeline * Containerize and deploy production-grade ...

Staples, Inc.

Sr Manager AI Platforms

Framingham, MA · Remote

$130K - $171K/yr

Experience with GenAI platforms, large language models, agentic AI frameworks, prompt orchestration, vector databases, retrieval-augmented generation, or AI application development patterns.

Staples, Inc.

Sr Manager AI Platforms

Framingham, MA · On-site

$130K - $171K/yr

Experience with GenAI platforms, large language models, agentic AI frameworks, prompt orchestration, vector databases, retrieval-augmented generation, or AI application development patterns.

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All JobsVector Databases JobsVector Databases Jobs in Sterling, 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 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 Sterling, MA look for? The top searched job categories for Vector Databases jobs in Sterling, MA are:
What cities near Sterling, MA are hiring for Vector Databases jobs? Cities near Sterling, MA with the most Vector Databases job openings:
Vector Databases jobs near you
AI Architect

AI Architect

Talent Software Services, Inc

Worcester, MA • Remote

Other

Posted 11 days ago

Job description

Job Title - AI Architect
Location - Work At Home - MA, Work At Home, Massachusetts, United States of America, 01604
Duration: 3 months Temporary to Permanent position for the right candidate. 
This role is remote - required to work Eastern hours

The AI Architect is responsible for designing, developing, and governing enterprise-grade AI solutions that align with business strategy. This role blends deep technical expertise in artificial intelligence, machine learning, data, and cloud architecture with strong product intuition, security awareness, and leadership. The AI Architect ensures that AI initiatives are scalable, ethical, secure, cost-efficient, and integrated into the broader enterprise ecosystem.

Key Responsibilities
  • AI Strategy & Solution Architecture
    • Define and evolve the enterprise AI architecture, ensuring alignment with business, data, and technology strategies.
    • Design scalable, secure, and compliant automation solutions to streamline processes across the enterprise.
    • Architect end-to-end AI solutions including data engineering, RAG model development, model operations (MLOps), and lifecycle management.
    • Partner with business, product, and engineering teams to translate business problems into appropriate AI/ML approaches.
    • Develop reference architectures and reusable patterns for generative AI, Agentic AI, predictive models, conversational systems, and intelligent automation.
  • Technical Leadership
    • Provide architectural oversight across AI/ML projects to ensure consistency, performance, and maintainability.
    • Evaluate and select AI technologies, frameworks, cloud services, vector databases, LLM orchestration frameworks, and tooling.
    • Support development teams on model selection, training pipelines, prompt engineering, fine-tuning, RAG (Retrieval-Augmented Generation), and evaluation methodologies.
    • Mentor engineers, analysts, and product teams on AI best practices.
  • Data, Integration & Platforms
    • Partner with data architects and engineering to ensure robust data pipelines, governance, feature stores, and architecture.
    • Design secure and performant integration between AI models and enterprise systems (APIs, microservices, events).
  • Governance & Compliance
    • Ensure AI solutions adhere to enterprise security standards, data privacy policies, and regulatory requirements.
    • Implement responsible AI guardrails, fairness checks, explainability frameworks, and monitoring.
    • Develop and maintain automation governance frameworks, documentation, and audit trails.
  • Operations & Optimization
    • Define MLOps / LLMOps standards including CI/CD pipelines, model monitoring, drift detection, observability, and rollback processes.
    • Drive continuous improvement of model performance, cost optimization, and operational efficiency.
    • Establish KPIs, telemetry, and feedback loops for production AI systems.
  • Collaboration & Enablement
    • Partner with IT, compliance, operations, and customer service teams to align automation initiatives with business goals.
    • Mentor and guide developers and analysts to build a center of excellence (CoE) for automation.
Required Qualifications
  • Bachelor’s degree in Computer Science, Engineering, or a related technical field.
  • 5 years of experience in application development, engineering, or solution delivery roles.
  • 1 year of hands-on experience in AI/ML engineering, data science, or AI solution architecture.
  • Strong hands-on experience with machine learning frameworks and LLM platforms (e.g., OpenAI, Azure AI Foundry, Copilot Studio/Agent Builder, or comparable generative AI ecosystems).
  • Deep expertise in cloud platforms, particularly Microsoft Azure, and modern architectural patterns (microservices, event-driven architectures, API-first design).
  • Proficiency in one or more of the following: Python, Azure Machine Learning, or related AI/ML tooling.
  • Experience with MLOps/LLMOps ecosystems, including tools such as MLflow, Kubernetes, LangChain, vector databases, and feature stores.
  • Strong hands-on experience with ML frameworks, LLM platforms - OpenAI, MSFT/Azure Cloud foundry, Copilot Studio Agent builder, low code/no code platforms, and generative AI tools.
  • Background in RAG systems, model fine-tuning, embeddings, vector storage, and retrieval optimization.
Preferred Qualifications
  • Experience in enterprise-wide AI programs or platform buildouts.
  • Strong understanding of data governance, privacy, security, and model risk management.
  • Prior experience with large-scale transformation programs.

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