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# Python/GenAI Engineer

- **Company:** SGA Inc.
- **Location:** Irving, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Continuous Integration, Python (Programming Language), Open Source Technology, Tensorflow, Software Construction, Software Engineering, Systems Integration, TypeScript, Web Applications, Google Cloud, Pytorch, Large Language Models, Prompt Engineering, Generative AI, Backend, Git, HuggingFace, Data Management, Machine Learning Operations, Virtual Agents, Docker, Microservices
- **Published:** May 20, 2026
- **Apply:** https://www.dice.com/job-detail/f02f79ff-b0f2-44c4-b433-5a8ef6374f3b

## About the Role

* Technical Proficiency: Strong command of Python, PyTorch, TensorFlow, and Hugging Face libraries.
 * GenAI Experience: Hands-on experience with LangChain, LlamaIndex, vector databases, and fine-tuning techniques (LoRA, QLoRA).
 * API & Backend: Proven ability to integrate AI models into web applications via APIs (OpenAI, Anthropic).
 * Software Engineering: Solid understanding of software engineering best practices, including Git, CI/CD, and Docker.

Preferred Skills:

 * Experience with multimodal AI models (image, video, audio generation).
 * Published AI/LLM research or contributions to open-source AI projects.
 * Background in AI governance or safety policy development.

## Description

Software Guidance & Assistance, Inc., (SGA), is searching for an Sr. Python/GenAI Engineer for a RIGHT TO HIRE assignment with one of our premier IBanking clients in Irving, TX ., * AI Agent Development: Build and orchestrate AI agents using frameworks like LangChain, AutoGen, or CrewAI, implementing self-healing workflows (e.g., Act-Verify-Refine loops).

 * LLM Integration & Backend: Develop robust backend systems using Python and TypeScript, integrating LLMs into microservices architectures.
 * Data Management for LLMs: Utilize vector databases (Pinecone, Milvus, Weaviate) for agent memory and architect Retrieval-Augmented Generation (RAG) pipelines to enhance LLM accuracy and contextual understanding.
 * Prompt Engineering: Design and optimize prompt strategies, including automated evaluation frameworks, for high-quality LLM output.
Context Engineering: Manage LLM information ecosystems, including system prompts, RAG implementation, and conversation history.

 * MLOps & Deployment: Oversee the end-to-end lifecycle of generative models, focusing on inference speed, cost-efficiency, and scalability on cloud platforms (AWS, Google Cloud Platform, Azure).
 * AI Ethics & Compliance: Ensure adherence to security standards, IP regulations, and safety guidelines for all generative models.
Tool Orchestration: Define and manage the API/tool access for AI agents to optimize accuracy.


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