NRG is looking to build and scale Generative AI (GenAI) applications to deliver innovative solutions that make customers' lives easier, helping them power, protect, and intelligently manage their homes and businesses. The Applied AI Engineer will be instrumental in designing, building, and scaling these GenAI applications, while also contributing to enterprise standards and the maturity of AI engineering practices.
Requirements
- 3-5 years hands-on experience building GenAI/LLM-based applications, with proven success from PoC to production deployment.
- Proficiency in designing retrieval pipelines (document loaders, chunking strategies, embeddings, vector databases like FAISS, Pinecone, ChromaDB).
- Expertise in LLM APIs (OpenAI, Claude, Gemini, etc.), prompt engineering, and fine-tuning.
- Experience with cloud platforms (GCP, Azure), containerization (Docker, Kubernetes), and MLOps (CI/CD, monitoring).
- Strong understanding of API design, microservices, and enterprise integration patterns.
- Familiarity with version control systems (e.g., Git, Azure DevOps).
- Demonstrated ability to build and scale AI solutions in production.
Responsibilities
- Translate business requirements into robust, scalable AI solutions using RAG, embeddings, vector search, and fine-tuning.
- Design, prototype, and implement LLM-driven applications with multi-step agent workflows and orchestration frameworks (e.g., LangGraph, LangChain, LlamaIndex).
- Build and maintain APIs, services, and reusable components in Python to support AI applications.
- Deploy and monitor AI models in cloud-native environments (GCP, Azure) leveraging Kubernetes, serverless, and MLOps pipelines.
- Continuously evaluate model/system performance and implement improvements.
- Contribute to the design of modular and reusable AI architectures across projects.
- Establish and follow engineering best practices for GenAI development, testing, deployment, and monitoring.
Other
- Hybrid work model: Onsite in Houston office 3 days per week.
- Open, collaborative team environment.
- Partner with cross-functional teams to integrate AI capabilities into enterprise applications.
- Work closely with business stakeholders to translate challenges into AI-powered solutions.
- Ensure AI applications align with security, compliance, and responsible AI standards.
