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Vector Databases Jobs in Dayton, OH (NOW HIRING)

Birlasoft

Technical Specialist-App Development

Kettering, OH · On-site

$45 - $58.25/hr

You will lead the integration of Generative AI models, vector databases, and autonomous AI agents to drive our next-generation product features. Key Responsibilities * Backend Development: Design ...

RIT Solutions, Inc.

Senior AI Engineer

Mason, OH · On-site

$98K - $134K/yr

... vector databases • Build multi-step, tool-using agents (planning, execution, memory) using frameworks such as LangChain • Integrate AI systems with APIs, backend services, and cloud platforms • ...

ClifyX

Machine Learning Engineer

Dayton, OH · On-site

... vector databases, hybrid semantic neural architectures, or agentic AI systems. Company : ClifyX provides innovative business solutions which satisfy requirements for mission-critical reliability ...

Infosys

Senior AI Engineer

Mason, OH · On-site

$115K - $151K/yr

Architect and deliver end-to-end LLM-powered applications and agentic workflows using Python Design and implement RAG pipelines over enterprise data using embeddings and vector databases Build multi ...

Cryptic Vector

Full Stack Developer

Miamisburg, OH

Description At Cryptic Vector, we are dedicated to mission success. We take the time to understand ... Troubleshoot and debug issues across the full stack, from UI rendering to database performance and ...

Cryptic Vector

Full Stack Developer

Miamisburg, OH · On-site

At Cryptic Vector, we are dedicated to mission success. We take the time to understand our ... Troubleshoot and debug issues across the full stack, from UI rendering to database performance and ...

RELX

AI Data Analyst

Dayton, OH · On-site

Does not act as the primary technical owner for RAG frameworks, vector databases, or embedding strategies. * Does not make final governance or compliance decisions independently. Requirements:

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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 Dayton, OH?

For Vector Databases jobs in Dayton, OH, the most frequently searched job titles are:

What job categories do people searching Vector Databases jobs in Dayton, OH look for?

The top searched job categories for Vector Databases jobs in Dayton, OH are:

What cities near Dayton, OH are hiring for Vector Databases jobs?

Cities near Dayton, OH with the most Vector Databases job openings:

Vector Databases jobs near you

AI/ML Engineer, onsite in Woodland Hills, CA / Mason, OH - Full-time

Ztek Consulting

Mason, OH • On-site

Other

This job post has expired today. Applications are no longer accepted.

Job description

Role: AI/ML Engineer, onsite in Woodland Hills, CA / Mason, OH - Full-time
Job description: 427836/-BH

Experience Required: 8 - 20 + Years

Must Have Technical/Functional Skills

  • Python (Expert level)
  • Machine Learning & Model Training

o Training, evaluation, fine tuning

o Tagging and labeling workflows

  • Generative AI & LLMs

o Prompt engineering for LLM-based applications

  • Document Processing

o Document extraction, parsing, and chunking

o Handling structured & unstructured data

  • Embeddings & Vector Search

o Embedding generation

o Vector database integration

  • Databases

o Vector Databases

o MongoDB

  • Production-grade ML Engineering

o Scalable, production-ready ML/GenAI solutions

Roles & Responsibilities

This role is for a hands-on AI/ML Engineer who will design, build, and deploy productiongrade Machine Learning and Generative AI solutions. The candidate must have strong Python expertise and practical experience taking ML and GenAI use cases from development to deployment.

The role focuses heavily on LLM-based applications, including prompt engineering, document processing pipelines, and embedding-based search solutions. The engineer will work with both structured and unstructured data, building pipelines for document extraction, parsing, and chunking, and integrating ML models with Vector Databases and MongoDB.

An ideal candidate is someone who understands end-to-end ML workflows from data preparation, tagging, and labeling, through model training, evaluation, and fine-tuning while ensuring solutions are scalable, high quality, and production ready.

Key Responsibilities

  • Design and implement AI/ML solutions using Python and modern ML frameworks
  • Develop and optimize Prompt Engineering strategies for LLM-based systems
  • Build and deploy Retrieval-Augmented Generation (RAG) pipelines
  • Integrate LLMs via APIs (Azure OpenAI preferred) into enterprise applications
  • Develop and orchestrate Agent ic AI workflows with tool/function calling
  • Implement vector search solutions using Vector Databases
  • Ensure CI/CD integration and cloud deployment (Azure preferred)
  • Establish observability, monitoring, and evaluation frameworks for AI systems
  • Collaborate with cross-functional teams to deliver production-ready AI features

Generic Managerial Skills, If any

  • Ability to explain complex ML / GenAI concepts to nontechnical stakeholders and collaborate effectively with crossfunctional teams.
  • Strong analytical thinking to break down ambiguous business problems into workable ML or GenAI solutions.
  • Takes endtoend responsibility for solutions from design to production readiness without constant supervision.
  • Works well with data engineers, product owners, and platform teams to deliver integrated, scalable solutions.
  • Actively keeps up with evolving ML, LLM, and GenAI technologies and improves skills proactively.

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