Generative AI Engineer (LLM & Agentic Systems) NEW!

Generative AI Engineer (LLM & Agentic Systems)Austin,TXDatePosted : 4/10/2026 5:25:38 PMJobNumber : DTS1017187687JobType : W2-Contract to HireSkills: LLMs, RAG, LangChain, LangGraph, CrewAI, AutoGPT, Python, OpenAI, Hugging Face, Azure AI, Autonomous Agents, Context Engineering, MCP, AI Governance.Job DescriptionWe are seeking a highly skilled Senior AI Engineer to design, build, and deploy production-grade AI systems powered by Large Language Models (LLMs). This role focuses on developing autonomous agents, multi-agent systems, and Retrieval-Augmented Generation (RAG) architectures.The ideal candidate will have hands-on experience with frameworks like LangChain, LangGraph, CrewAI, or AutoGPT, strong Python expertise, and a deep understanding of context engineering, AI governance, and scalable enterprise AI solutions.Key ResponsibilitiesDesign and deploy production-ready autonomous AI agents and agentic workflows.Build and optimize RAG architectures using vector databases.Develop solutions using LLM frameworks such as LangChain, LangGraph, CrewAI, or AutoGPT.Integrate LLMs via APIs (OpenAI, Hugging Face, Azure AI) into enterprise applications.Implement and extend the Model Context Protocol (MCP) for secure and standardized data access.Apply context engineering techniques to improve model performance and response quality.Implement AI guardrails, content filtering, and safety mechanisms.Ensure compliance with data privacy standards, including handling of PII and PHI.Collaborate with cross-functional teams to design scalable enterprise AI architectures.Optimize LLM performance, token usage, and cost efficiency.Contribute to AI governance, model lifecycle management, and evaluation frameworks.Required Qualifications4+ years of experience in AI/ML engineering or advanced data science.Proven experience building and deploying production-grade autonomous agents.Strong expertise in context engineering and prompt design strategies.Hands-on experience with LangChain, LangGraph, CrewAI, AutoGPT, or similar frameworks.Experience implementing RAG architectures with vector databases.Proficiency in Python and AI/ML libraries (OpenAI, Hugging Face, Azure AI).Experience integrating LLMs via APIs into real-world applications.Knowledge of AI governance, model lifecycle management, and evaluation.Experience implementing AI safety controls, guardrails, and content filtering.Understanding of data privacy and secure handling of sensitive data (PII/PHI).Preferred QualificationsExperience building multi-agent or agentic AI workflows.Experience optimizing LLM cost, latency, and token usage.Familiarity with enterprise AI deployment patterns and scalability.Exposure to secure AI architectures and compliance frameworks.