AI Database Platform Lead

MethodHub; Dallas (TX); On-site; USD 150,000 - 190,000; Full time

Job summary

MethodHub in Dallas, TX is seeking a senior Enterprise Data Architect to lead architecture and design of data platforms that support AI, ML, and GenAI applications.

You will drive scalable, secure data solutions, mentor engineers, and collaborate across API, data engineering, and cloud teams to modernize environments and enable reliable AI-powered insights.

Qualifications

• Strong experience in database, data platform, or enterprise platform architecture.
• Experience designing and implementing highly scalable enterprise database solutions.
• Strong understanding of relational and NoSQL database technologies.
• Experience with modern cloud-based or hybrid data platforms.
• Hands-on understanding of AI/ML and Generative AI architectures.
• Experience integrating AI applications with enterprise databases and data sources.
• Understanding of LLMs, RAG, embeddings, vector databases/vector search, and AI data pipelines.
• Strong knowledge of APIs, data integration, data modeling, and distributed systems.
• Experience with database performance, scalability, high availability, and disaster recovery concepts.
• Understanding of data security, governance, authentication, authorization, and enterprise security practices.
• Experience leading architecture and technical design discussions across multiple engineering teams.
• Strong troubleshooting, communication, and stakeholder-management skills.
• Ability to work both as a technical lead and hands-on contributor.

Responsibilities

• Lead the architecture and design of enterprise database and data platforms supporting AI, ML, and Generative AI applications.
• Design scalable solutions for integrating AI platforms with structured and unstructured enterprise data.
• Provide technical leadership across database, data engineering, AI, and cloud platform initiatives.
• Evaluate existing database and data environments and recommend modernization and optimization approaches.
• Design data access and integration patterns for AI/GenAI applications, including APIs, data pipelines, vector search, and retrieval-based solutions.
• Support the implementation of RAG and other AI architectures that require secure access to enterprise data.
• Work with engineering teams to address database performance, scalability, availability, and reliability.
• Establish architecture standards and best practices for AI and database platform integration.
• Ensure appropriate security, governance, access controls, and data protection are incorporated into platform designs.
• Collaborate with application, infrastructure, DevOps, data engineering, and AI/ML teams.
• Lead technical discussions, architecture reviews, proof-of-concepts, and solution evaluations.
• Troubleshoot complex platform and integration issues and provide technical direction to engineering teams.
• Mentor technical team members and provide guidance throughout design, development, and implementation.

Skills

Enterprise architecture

DB design

Relational databases

NoSQL databases

Cloud platforms

AI architectures

AI integration

LLMs & embeddings

APIs

Data modeling

Performance & DR

Security & governance

Leadership

Mentorship

Tools

PostgreSQL

Oracle

SQL Server

MySQL

Job description

Work Authorization: U.S. Citizens and Green Card holders only

Duration: Long-Term Contract

Key Responsibilities

• Lead the architecture and design of enterprise database and data platforms supporting AI, ML, and Generative AI applications.
• Design scalable solutions for integrating AI platforms with structured and unstructured enterprise data.
• Provide technical leadership across database, data engineering, AI, and cloud platform initiatives.
• Evaluate existing database and data environments and recommend modernization and optimization approaches.
• Design data access and integration patterns for AI/GenAI applications, including APIs, data pipelines, vector search, and retrieval-based solutions.
• Support the implementation of RAG and other AI architectures that require secure access to enterprise data.
• Work with engineering teams to address database performance, scalability, availability, and reliability.
• Establish architecture standards and best practices for AI and database platform integration.
• Ensure appropriate security, governance, access controls, and data protection are incorporated into platform designs.
• Collaborate with application, infrastructure, DevOps, data engineering, and AI/ML teams.
• Lead technical discussions, architecture reviews, proof-of-concepts, and solution evaluations.
• Troubleshoot complex platform and integration issues and provide technical direction to engineering teams.
• Mentor technical team members and provide guidance throughout design, development, and implementation.

Required Qualifications

• Strong experience in **database, data platform, or enterprise platform architecture**.
• Experience designing and implementing highly scalable enterprise database solutions.
• Strong understanding of relational and NoSQL database technologies.
• Experience with modern cloud-based or hybrid data platforms.
• Hands‑on understanding of **AI/ML and Generative AI architectures**.
• Experience integrating AI applications with enterprise databases and data sources.
• Understanding of **LLMs, RAG, embeddings, vector databases/vector search, and AI data pipelines**.
• Strong knowledge of APIs, data integration, data modeling, and distributed systems.
• Experience with database performance, scalability, high availability, and disaster recovery concepts.
• Understanding of data security, governance, authentication, authorization, and enterprise security practices.
• Experience leading architecture and technical design discussions across multiple engineering teams.
• Strong troubleshooting, communication, and stakeholder‑management skills.
• Ability to work both as a technical lead and hands‑on contributor.

Preferred Qualifications

• Experience with major cloud platforms such as AWS, Azure, or GCP.
• Experience with enterprise database technologies such as Oracle, PostgreSQL, SQL Server, MySQL, or similar platforms.
• Exposure to modern data platforms such as Snowflake, Databricks, or equivalent technologies.
• Experience with vector databases or vector‑enabled database platforms.
• Experience building or supporting production **GenAI/RAG solutions**.
• Familiarity with LLM frameworks, AI APIs, model integration, and agentic AI concepts.
• Experience with Kubernetes, containers, CI/CD, Infrastructure as Code, and DevOps practices.
• Experience leading enterprise platform modernization or cloud transformation initiatives.