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Vector Databases Jobs in Missouri (NOW HIRING)

Jobtailor

Lead Data and Ontology Engineer

California, MO · On-site

$140 - $210/hr

Develop and implement strategies for using vector databases and graph databases to enable powerful LLM‑augmented search and reasoning. * Partner with data engineering, AI, and business teams to ...

Jobtailor

Lead Data Engineer - Governance

California, MO · On-site

$140 - $190/hr

Collaborate with Data Engineering teams to implement governance in federated querying, GraphQL interfaces, vector databases, and RAG pipelines * Define and maintain enterprise data stewardship ...

Mylitm

MLOps Engineer

California, MO · On-site

$140 - $210/hr

Experience with feature stores, vector databases, and data versioning (DVC, LakeFS). * Background in cost optimization for GPU inference workloads. #J-18808-Ljbffr

Confiz Limited

AI Solutions Architect

California, MO · On-site

$180 - $270/hr

Evaluate and select appropriate AI frameworks, LLM providers (OpenAI, Anthropic, Azure AI Foundry, etc.), and vector databases for use-case fit. * Establish best practices for model lifecycle ...

Jobtailor

Principal Data Engineer - MDM

California, MO · On-site

$180 - $240/hr

Lead evaluation, selection, and adoption of graph technologies, semantic platforms, vector databases, and AI infrastructure required to support enterprise‑scale workloads. * Define and drive the ...

Jobtailor

Senior AI Product Architect

California, MO · On-site

$180 - $260/hr

Provide direction for distributed search, semantic search, vector databases, embeddings, hybrid search, and retrieval-augmented generation * Partner with Data Science and AI Engineering to ...

Merck Group

Staff Software Engineer

Saint Louis, MO · On-site

$110K - $185K/yr

Vector Databases like Qdrant Nice to have Skills: * Experience with Kubernetes and container orchestration. * Familiarity with event-driven architectures and messaging platforms such as Kafka.

Jobgether

Drive technical decisions around embeddings, vector databases, retrieval strategies, and related AI infrastructure. * Ensure AI workflows are reproducible, testable, maintainable, and aligned with ...

Jobtailor

AI Software Engineer

Dearborn, MO · On-site

$110 - $150/hr

Experience with vector search, hybrid retrieval architectures, or vector databases (Chroma, Qdrant, Pinecone, pgvector). * Experience working with GCP services (Vertex AI, Cloud Run, and BigQuery) or ...

Jobtailor

Mid-Level AI Platform Engineer

California, MO · On-site

$150 - $190/hr

... vector databases * Understanding of agentic AI concepts and exposure to frameworks such as LangChain or LangGraph * Experience with cloud platforms (Azure and/or AWS) * Knowledge of distributed ...

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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 Missouri?

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

What job categories do people searching Vector Databases jobs in Missouri look for?

The top searched job categories for Vector Databases jobs in Missouri are:

What cities in Missouri are hiring for Vector Databases jobs?

Cities in Missouri with the most Vector Databases job openings:

Vector Databases jobs near you
Infographic showing various Vector Databases job openings in Missouri as of August 2026, with employment types broken down into 89% Full Time, 5% Part Time, and 6% Contract. Highlights an 83% Physical, 6% Hybrid, and 11% Remote job distribution.

Lead Data and Ontology Engineer

California, MO • On-site

Jobtailor
See more jobs at Jobtailorexternal

$140 - $210/hr

Other

Posted 9 days ago

Job description


  • Lead the design, development, and governance of enterprise ontologies, semantic layers, and knowledge graphs.

  • Combine deep semantic modeling expertise with hands‑on implementation of graph technologies, vector databases, and federated data architectures.

  • Design and build enterprise ontologies and semantic models that align business objectives with technical implementation.

  • Lead the creation and maintenance of semantic layer graphs, knowledge graphs, and entity graphs.

  • Develop and implement strategies for using vector databases and graph databases to enable powerful LLM‑augmented search and reasoning.

  • Partner with data engineering, AI, and business teams to translate business goals into ontological models.

  • Contribute to the development of a unified data access layer that supports querying across diverse data sources.

  • Implement and enforce enterprise security models through the ontology and semantic layer.

  • Collaborate on AI integration initiatives, including building ontology‑driven agents.

  • Establish ontology governance processes and contribute to the internal Data Marketplace.


Requirements

  • 7+ years of experience in data engineering, data architecture, semantic technologies, knowledge engineering, or related fields with a strong technical implementation background.

  • Deep expertise in ontology modeling (OWL, RDF, SKOS, SHACL) and graph technologies.

  • Strong understanding of the differences between semantic layer graphs, knowledge graphs, and entity graphs and when to apply each.

  • Hands‑on experience with graph databases (Neo4j, Neptune, etc.) and vector databases for semantic search and LLM integration.

  • Proficiency in designing and implementing Data Catalogs and semantic metadata management solutions.

  • Experience building solutions on top of federated data architectures involving relational (PostgreSQL, Snowflake), document (MongoDB), object (S3), and streaming (Kafka) systems.

  • Demonstrated ability to translate complex business goals into ontological designs that accelerate delivery of business value.

  • Strong programming skills, particularly Python, SPARQL, Cypher, GraphQL, and SQL.

  • Experience with modern data platforms, cloud services (AWS preferred), and infrastructure‑as‑code practices.

  • Solid understanding of data governance, security (RBAC/ABAC, dynamic masking), and compliance in enterprise environments.

  • Excellent communication skills with the ability to bridge business stakeholders and technical teams.


Core Competencies

Demonstrates expertise in ontology modeling, semantic technologies, and graph databases to design and implement enterprise ontologies and knowledge graphs that align with business objectives. Proficient in data governance, security models, and collaboration with cross‑functional teams to drive AI integration and data access strategies.


Highest-signal resume keywords

  • Ontology Modeling (OWL, RDF, SKOS, SHACL)

  • Graph Technologies (Neo4j, Neptune)

  • Vector Databases

  • Data Governance and Security (RBAC/ABAC)

  • Programming Skills (Python, SPARQL, Cypher, GraphQL, SQL)


ATS Optimization Keywords
Hard Skills

  • Ontology Modeling

  • Graph Technologies

  • Vector Databases

  • Data Catalogs

  • Semantic Metadata Management

  • Federated Data Architectures

  • Programming (Python, SPARQL, Cypher, GraphQL, SQL)

  • Data Governance

  • Security Models

  • Cloud Services (AWS)


Soft Skills

  • Excellent Communication Skills


Industry Keywords

  • Data Engineering

  • Data Architecture

  • Semantic Technologies

  • Knowledge Engineering

  • Enterprise Ontologies


Tools & Technologies

  • Neo4j

  • Neptune

  • PostgreSQL

  • Snowflake

  • MongoDB

  • S3

  • Kafka

  • AWS

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