Generative AI Engineer
Job in
Houston, Harris County, Texas, 77246, USA
Listed on 2026-03-08
Listing for:
Smart IT Frame LLC
Full Time
position Listed on 2026-03-08
Job specializations:
-
Software Development
AI Engineer, Machine Learning/ ML Engineer, Cloud Engineer - Software, Software Engineer
Job Description & How to Apply Below
We are seeking a skilled Generative AI Developer / Engineer with a strong software engineering background in Java or .NET
, who has transitioned into building Generative AI solutions
. The ideal candidate will have hands‑on experience designing, developing, and deploying AI‑driven applications using modern LLMs and AI frameworks.
Key Responsibilities:
- Design, develop, and deploy Generative AI solutions using Large Language Models (LLMs)
- Build AI‑powered applications such as chatbots, copilots, summarization tools, and intelligent automation systems
- Integrate Generative AI models with Java or .NET–based backend systems
- Work with APIs and SDKs from LLM providers (OpenAI, Azure OpenAI, Anthropic, etc.)
- Implement prompt engineering
, fine‑tuning, and RAG (Retrieval‑Augmented Generation) pipelines - Collaborate with product, data, and cloud teams to deliver scalable AI solutions
- Ensure performance, security, and scalability of AI‑driven applications
- Stay updated with emerging trends in Generative AI and apply best practices
Required Skills &
Qualifications:
- Strong experience as a Java Developer or .NET Developer (minimum 3–5 years)
- 2–3 years of hands‑on experience in Generative AI development
- Proficiency with LLMs
, prompt engineering, and AI workflows - Experience with Python for AI/ML development
- Hands‑on experience with frameworks like Lang Chain, Llama Index, Semantic Kernel
, or similar - Knowledge of REST APIs
, microservices, and cloud‑based architectures - Familiarity with vector databases (Pinecone, FAISS, Chroma, Azure AI Search, etc.)
- Experience working on Azure, AWS, or GCP environments
- Strong problem‑solving and communication skills
Good to Have:
- Experience with MLOps / AIOps practices
- Knowledge of AI model fine‑tuning and evaluation techniques
- Exposure to CI/CD pipelines and containerization (Docker, Kubernetes)
- Understanding of data privacy, security, and AI governance
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