Generative AI Engineer

GBIT (Global Bridge InfoTech Inc); McKinney (TX); On-site; USD 130,000 - 180,000; Full time

Job summary

GBIT (Global Bridge InfoTech Inc) is seeking an experienced Generative AI Engineer to design, develop, and deploy AI-powered solutions for enterprise applications in a fast-paced environment.

You will work with OpenAI/Azure OpenAI, RAG, embeddings, and AI agents to transform business processes, integrating GenAI capabilities across APIs, databases, and cloud platforms while prioritizing security and governance.

Qualifications

• 5+ years of experience in software/application development or engineering
• 2+ years hands-on experience with Generative AI / LLM technologies
• Strong programming experience with Python and/or Java
• Hands-on experience with OpenAI, Azure OpenAI, Anthropic, or other LLM platforms
• Experience developing RAG-based applications and working with embeddings and vector databases
• Strong understanding of prompt engineering and LLM application development
• Experience integrating AI solutions with enterprise applications and APIs
• Experience with REST APIs, microservices, databases, and enterprise application architectures
• Experience with at least one major cloud platform: Azure, AWS, or GCP
• Understanding of Git, CI/CD, testing, and deployment automation
• Strong understanding of data security, authentication, authorization, and enterprise application integration

Responsibilities

• Design, develop, and implement Generative AI solutions for enterprise applications and business use cases
• Develop applications using Large Language Models (LLMs) such as OpenAI, Azure OpenAI, Anthropic, or similar models
• Build and implement Retrieval‑Augmented Generation (RAG) solutions using enterprise data sources
• Develop AI agents, copilots, and intelligent automation solutions for enterprise workflows
• Implement prompt engineering, prompt optimization, grounding, context management, and model evaluation techniques
• Integrate GenAI capabilities with existing enterprise applications, APIs, databases, and business systems
• Work with vector databases and search technologies such as Azure AI Search, Pinecone, Weaviate, or similar platforms
• Develop REST APIs, microservices, and backend components to integrate AI capabilities into enterprise applications
• Implement AI solutions using cloud platforms such as Azure, AWS, or GCP
• Work with application and data teams to securely connect LLM applications to enterprise data
• Implement security, access control, data privacy, and governance requirements for enterprise AI solutions
• Monitor and optimize AI applications for performance, scalability, reliability, cost, and response quality
• Collaborate with architects, developers, data engineers, product managers, and business stakeholders
• Participate in the complete SDLC, including requirements analysis, design, development, testing, deployment, and production support

Skills

Generative AI

LLMs

RAG

Prompt engineering

AI agents

Cloud platforms

Python

Java

OpenAI

Azure OpenAI

Anthropic

Embeddings

Vector databases

REST APIs

Microservices

Databases

Git

CI/CD

Testing

Deployment automation

Data security

Authentication

Authorization

Enterprise integration

Job description

We are seeking an experienced Generative AI Engineer with strong expertise in building and integrating AI-powered solutions within enterprise applications and business environments. The ideal candidate will have hands‑on experience with Generative AI, LLMs, RAG, prompt engineering, AI agents, and cloud platforms, along with a solid understanding of enterprise software development and integration.

The candidate will work closely with application development, architecture, data, and business teams to design, develop, and deploy scalable GenAI solutions that improve enterprise workflows and business processes.

Key Responsibilities:

• Design, develop, and implement Generative AI solutions for enterprise applications and business use cases.
• Develop applications using Large Language Models (LLMs) such as OpenAI, Azure OpenAI, Anthropic, or similar models.
• Build and implement Retrieval‑Augmented Generation (RAG) solutions using enterprise data sources.
• Develop AI agents, copilots, and intelligent automation solutions for enterprise workflows.
• Implement prompt engineering, prompt optimization, grounding, context management, and model evaluation techniques.
• Integrate GenAI capabilities with existing enterprise applications, APIs, databases, and business systems.
• Work with vector databases and search technologies such as Azure AI Search, Pinecone, Weaviate, or similar platforms.
• Develop REST APIs, microservices, and backend components to integrate AI capabilities into enterprise applications.
• Implement AI solutions using cloud platforms such as Azure, AWS, or GCP.
• Work with application and data teams to securely connect LLM applications to enterprise data.
• Implement security, access control, data privacy, and governance requirements for enterprise AI solutions.
• Monitor and optimize AI applications for performance, scalability, reliability, cost, and response quality.
• Collaborate with architects, developers, data engineers, product managers, and business stakeholders.
• Participate in the complete SDLC, including requirements analysis, design, development, testing, deployment, and production support.

Required Qualifications:

• 5+ years of experience in software/application development or engineering.
• 2+ years of hands‑on experience working with Generative AI / LLM technologies.
• Strong programming experience with Python and/or Java.
• Hands‑on experience with OpenAI, Azure OpenAI, Anthropic, or other LLM platforms.
• Experience developing RAG‑based applications and working with embeddings and vector databases.
• Strong understanding of prompt engineering and LLM application development.
• Experience integrating AI solutions with enterprise applications and APIs.
• Experience with REST APIs, microservices, databases, and enterprise application architectures.
• Experience with at least one major cloud platform: Azure, AWS, or GCP.
• Understanding of software engineering practices including Git, CI/CD, testing, and deployment automation.
• Strong understanding of data security, authentication, authorization, and enterprise application integration.