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Vector Databases Jobs in Columbus, OH (NOW HIRING)

The ideal candidate should have expertise in Python, Prompt Engineering, RAG pipelines, Vector Databases, and production-grade AI/ML deployment. This role focuses on building scalable AI solutions ...

AI Agent ML Engineer

Columbus, OH · On-site

$165K - $190K/yr

Build and maintain data pipelines, embeddings, and vector databases to support agent intelligence. * Optimize models for scalability, latency, and accuracy in production environments. * Champion ...

AI Full stack Software Engineer

Columbus, OH · Remote

$110.30K - $183.80K/yr

Communicate modern AI concepts (LLMs, vector databases etc.) to non-technical audiences * Iteratively craft effective prompts to maximize LLM performance and implement prompt caching strategies

AI Full stack Software Engineer

Columbus, OH · Remote

$110.30K - $183.80K/yr

Communicate modern AI concepts (LLMs, vector databases etc.) to non-technical audiences * Iteratively craft effective prompts to maximize LLM performance and implement prompt caching strategies

AI Full stack Software Engineer

Columbus, OH · Remote

$110.30K - $183.80K/yr

Communicate modern AI concepts (LLMs, vector databases etc.) to non-technical audiences * Iteratively craft effective prompts to maximize LLM performance and implement prompt caching strategies

AI Full stack Software Engineer

Columbus, OH · Remote

$110.30K - $183.80K/yr

Communicate modern AI concepts (LLMs, vector databases etc.) to non-technical audiences * Iteratively craft effective prompts to maximize LLM performance and implement prompt caching strategies

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Vector Databases information

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 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 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 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 Columbus, OH? For Vector Databases jobs in Columbus, OH, the most frequently searched job titles are:
What cities near Columbus, OH are hiring for Vector Databases jobs? Cities near Columbus, OH with the most Vector Databases job openings:
Sr AI/ML Engineer

Sr AI/ML Engineer

Diverse Lynx

Prospect, OH • On-site

$150K/yr

Other

This job post has expired 1 day ago. Applications are no longer accepted.


Job description

Role: Sr AI/ML Engineer
Location: Woodland Hills, CA / Mason, OH (Onsite)
Job Type: Fulltime
Salary: $150K + Benefits
Job Summary
We are looking for a hands-on Sr AI/ML Engineer with strong experience in Machine Learning, Generative AI, and LLM-based application development. The ideal candidate should have expertise in Python, Prompt Engineering, RAG pipelines, Vector Databases, and production-grade AI/ML deployment.
This role focuses on building scalable AI solutions involving document processing, embeddings, vector search, and enterprise AI integrations using modern ML and GenAI technologies.
Design and develop scalable AI/ML solutions using Python
Build and deploy LLM-based applications and RAG pipelines
Implement Prompt Engineering strategies for GenAI applications
Develop document extraction, parsing, chunking, and preprocessing pipelines
Integrate Vector Databases and embedding-based search solutions
Work with structured and unstructured data for AI workflows
Train, evaluate, and fine-tune ML models
Integrate OpenAI/Azure OpenAI APIs into enterprise applications
Develop Agentic AI workflows and tool/function calling solutions
Ensure production readiness, scalability, monitoring, and observability of AI systems
Collaborate with Data Engineers, Product Teams, and ML Engineers for end-to-end delivery
Support CI/CD and cloud deployment activities (Azure preferred)
Expert-level Python development
Strong Machine Learning and Model Training experience
Hands-on experience with Generative AI and Large Language Models (LLMs)
Prompt Engineering and RAG implementation experience
Experience with Embeddings and Vector Search
Knowledge of Vector Databases and MongoDB
Experience with document processing, parsing, and chunking
Strong understanding of production-grade ML deployment
Experience building scalable AI/GenAI solutions
Azure OpenAI / OpenAI integration experience
Experience with Agentic AI workflows
CI/CD and cloud deployment exposure
Knowledge of monitoring and observability frameworks
Experience with enterprise AI applications
Diverse Lynx LLC is an Equal Employment Opportunity employer. All qualified applicants will receive due consideration for employment without any discrimination. All applicants will be evaluated solely on the basis of their ability, competence and their proven capability to perform the functions outlined in the corresponding role. We promote and support a diverse workforce across all levels in the company.

Diverse Lynx logo

About Diverse Lynx

Sourced by ZipRecruiter

Diverse Lynx, based in Princeton, NJ, US, is a reputable company in the Information Technology sector. The firm, as reflected through its website diverselynx.com, specializes in delivering comprehensive IT solutions. These solutions range from IT consulting to robust digital transformation strategies, IT staffing, and full-time placements services. The company was established in 2008, and it prides itself on providing simplified, efficient technology solutions designed to meet the unique needs of each client.

Industry

It services

Company size

51 - 200 Employees

Headquarters location

Princeton, NJ, US

Year founded

2002

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