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# Generative AI Engineer

- **Company:** Technosoft Corporation
- **Location:** Houston, TX, United States
- **Experience:** Experienced
- **Salary:** $135,200.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Computer Programming, Software Debugging, Python (Programming Language), Open Source Technology, Performance Tuning, Software Product Management, Software Engineering, Systems Integration, Data Logging, Cloud Platform System, Large Language Models, Generative AI, Backend, Machine Learning Operations, Api Design, Data Pipelines, Microservices
- **Published:** June 11, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=94f35eee7ec12ce8

## About the Role

Do you have experience in Systems integration?, * 3-6+ years of software engineering experience

 * Strong programming skills (Python preferred)
 * Hands-on experience working with LLMs (e.g., OpenAI, Claude, open-source models)
 * Experience building APIs and working with backend systems
 * Familiarity with data pipelines, system integration, and cloud environments
 * Understanding of how AI/ML systems work (not just usage, but implementation)

Nice to Have

 * Experience with RAG architectures and vector databases
 * Familiarity with frameworks (LangChain, LlamaIndex, etc.)
 * Experience with agentic workflows or tool-using LLM systems
 * Exposure to fine-tuning or deploying open-source models
 * Experience in enterprise-scale environments, * Python backend development : 4 years (Required)
 * RAG systems: 3 years (Required)
 * building and deploying production LLM/GenAI systems: 3 years (Required)

## Description

Owns technical implementation of AI initiatives - from prototype * production deployment

Works closely with AI Product Delivery Leads, business stakeholders, and engineering teams

Deep, day-to-day development using LLMs, APIs, and AI frameworks

Duties:

 * Design and implement generative AI solutions (RAG, automation, agentic workflows)
 * Translate product requirements and use cases into scalable technical architectures
 * Build and optimize prompt strategies, LLM workflows, and agent-based systems
 * Develop Retrieval-Augmented Generation (RAG) pipelines using enterprise data
 * Integrate LLMs into applications via APIs and microservices
 * Collaborate with product and delivery leads to iterate on features and solutions
 * Evaluate and improve model performance (accuracy, latency, cost, reliability)
 * Implement logging, monitoring, and guardrails for AI systems
 * Rapidly prototype and test new AI capabilities
 * Leverage AI tools to accelerate development, debugging, and experimentation


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