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Generative AI Engineer
GBIT (Global Bridge InfoTech Inc)
1 hour ago
Full-time
On-site
McKinney, Texas, United States
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.
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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.
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