"The generative AI space is exploding, and companies are racing to embed intelligent agents into their products. Mastery of large\u2011language\u2011model frameworks and cloud\u2011native deployment is now a premium skill set. This contract role in Dallas offers a chance to work on cutting\u2011edge Agentic AI solutions while leveraging your deep LLM expertise.\n\n# Job Summary\nWe are seeking a seasoned GenAI Developer to design, implement, and optimize large\u2011language\u2011model (LLM) based agents. The role focuses on building end\u2011to\u2011end solutions using Agentic AI principles, integrating them with cloud platforms (preferably Azure) and ensuring production\u2011grade performance for enterprise clients.\n\n# Top 3 Critical Skills Table\n| Skill | Why it's critical | Mastery Level |\n|---|---|---|\n| LLM Frameworks | Foundation for building generative AI models and fine\u2011tuning them for specific tasks | Senior |\n| Agentic AI | Enables autonomous AI agents that can reason, plan, and act without constant human oversight | Senior |\n| Cloud (Azure) | Provides scalable compute, storage, and AI services needed for production deployments | Senior |\n\n# Interview Preparation\n1. **Explain how you would fine\u2011tune a pre\u2011trained LLM for a domain\u2011specific task.**\n *What the interviewer is looking for:* Understanding of data preprocessing, parameter selection, evaluation metrics, and deployment pipelines.\n2. **Describe the architecture of an autonomous agent that can retrieve information, reason, and execute actions.**\n *What the interviewer is looking for:* Knowledge of perception\u2011planning\u2011action loops, tool usage APIs, and safety/guardrails.\n3. **How do you ensure low latency and high throughput for LLM inference on Azure?**\n *What the interviewer is looking for:* Experience with Azure Machine Learning, scaling via AKS or VM clusters, model quantization, and caching strategies.\n4. **What are the key differences between Azure OpenAI Service, AWS Bedrock, and Google Vertex AI for LLM workloads?**\n *What the interviewer is looking for:* Comparative insight into pricing, model availability, security, and integration capabilities.\n5. **Walk through a debugging session where an AI agent produces hallucinated outputs.**\n *What the interviewer is looking for:* Ability to trace data flow, implement grounding techniques, and apply post\u2011processing filters.\n\n# Resume Optimization\n- GenAI Developer\n- Agentic AI\n- LLM Frameworks\n- Azure Machine Learning\n- AWS/GCP AI services\n- Prompt engineering\n- Model fine\u2011tuning\n- C2C contract experience\n- 9+ years AI/ML\n- H1B eligibility\n\n# Application Strategy\nWhen you email the recruiter, start with a polite greeting, attach your updated resume, and clearly reference the GenAI Developer role. Highlight your top skills\u2014such as LLM Frameworks, Agentic AI design, and Azure cloud deployment\u2014by mentioning concrete projects where you applied them. Make sure to map each key requirement from the JD to your own experience, and briefly note your eligibility to work in the U.S.\n\n# Career Roadmap\n| Current Role | Typical Experience | Core Focus | Next Position |\n|---|---|---|---|\n| GenAI Developer | 9+ years in AI/ML, LLMs, cloud | Build and fine\u2011tune LLM agents, cloud integration | Senior GenAI Engineer |\n| Senior GenAI Engineer | 12+ years, lead projects, mentor juniors | Architect large\u2011scale AI solutions, drive product strategy | AI Solutions Architect |\n| AI Solutions Architect | 15+ years, enterprise AI strategy | Oversee AI portfolio, align tech with business goals | Director of AI Innovation |\n"