What are the responsibilities and job description for the Generative AI Engineer (LLM & Agentic Systems) NEW! position at Dutech Systems?
Generative AI Engineer (LLM & Agentic Systems)
Austin,TX
DatePosted : 4/10/2026 5:25:38 PM
JobNumber : DTS1017187687
JobType : W2-Contract to Hire
Skills: LLMs, RAG, LangChain, LangGraph, CrewAI, AutoGPT, Python, OpenAI, Hugging Face, Azure AI, Autonomous Agents, Context Engineering, MCP, AI Governance.
Job Description
We 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 Responsibilities
Austin,TX
DatePosted : 4/10/2026 5:25:38 PM
JobNumber : DTS1017187687
JobType : W2-Contract to Hire
Skills: LLMs, RAG, LangChain, LangGraph, CrewAI, AutoGPT, Python, OpenAI, Hugging Face, Azure AI, Autonomous Agents, Context Engineering, MCP, AI Governance.
Job Description
We 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 Responsibilities
- Design 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.
- 4 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).
- Experience 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.